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6 episodes with inverse problems
2 episodes with inverse problems
7 episodes with inverse problems
3 episodes with inverse problems
2 episodes with inverse problems
19 episodes with inverse problems
La matematica raccontata da Caterina Fenu Caterina Fenu, nata a Cagliari nel 1983, si distingue come una figura emergente nel campo dell'analisi computazionale di reti complesse e problemi inversi di grandi dimensioni. Il suo percorso accademico è costellato di successi, a partire dalla laurea triennale e specialistica in Matematica conseguite presso l'Università di Cagliari, rispettivamente nel 2007 e nel 2011. Nel 2015, la sua dedizione agli studi culmina con il titolo di Dottore di Ricerca in Matematica e Calcolo Scientifico, ottenuto sotto la supervisione del Professor Giuseppe Rodriguez. La sua tesi, intitolata "Applications of low-rank approximation: Complex Networks and Inverse Problems", anticipa già il suo interesse per le tematiche che la porteranno alla ribalta. La sete di conoscenza di Caterina Fenu non si limita all'Italia. Durante il suo percorso formativo e di dottorato, ha trascorso periodi di ricerca all'estero, arricchendo il suo bagaglio di conoscenze e ampliando la sua visione. Una carriera ricca di successi Esperienze significative si concretizzano presso l'Università di Pisa, dove assume il ruolo di assegnista di ricerca dal 2015 al 2016, e presso l'Aachen Institute for Advanced Study in Computational Engineering Science della Rheinisch-Westfälischen Technischen Hochschule di Aachen, in Germania, dove opera come Postdoctoral Research Associate dal 2016 al 2017. Rientrata in Italia, Caterina Fenu prosegue la sua brillante carriera presso l'Università di Cagliari, ricoprendo il ruolo di borsista di ricerca dal 2017 al 2018 e di contrattista di ricerca presso il Consiglio Nazionale delle Ricerche di Napoli nel periodo gennaio-ottobre 2018. Dal 2018 al 2022, ottiene il titolo di Ricercatore a tempo determinato (tipologia A) presso il Dipartimento di Matematica e Informatica dell'Università di Cagliari, posizione che ricopre tutt'oggi con la qualifica di Ricercatore a tempo determinato. L'attività di ricerca di Caterina Fenu si concentra su due filoni principali: lo sviluppo di metodi computazionali per l'analisi di reti complesse e la risoluzione di problemi inversi di grandi dimensioni. La sua passione per questi ambiti si traduce in un corpus di pubblicazioni scientifiche di alto livello, che la rendono un punto di riferimento emergente nella comunità scientifica. A coronamento dei suoi successi, Caterina Fenu entra a far parte del Gruppo Nazionale per il Calcolo Scientifico (GNCS) nel 2011, testimonianza tangibile del suo contributo significativo al panorama STEM italiano. La Fenu rappresenta una figura di spicco nel panorama della ricerca computazionale, con un futuro radioso all'orizzonte. La sua dedizione, il suo talento e la sua passione per l'analisi di reti complesse e problemi inversi la pongono come protagonista indiscussa nel panorama accademico italiano e internazionale.
"The challenge is to really bring your heart into the classroom, show up as a person, show up with care." Dr. Roel Snieder discusses how to excel as a teacher (and professional) using the Teaching with Heart practices. In this unique and encouraging episode, we explore the Teaching with Heart project. Roel makes the case for creating a more nurturing and loving educational environment. This episode examines if and how the heart can play a role in mathematics, physics, and geophysics. Roel challenges the notion that teaching to outcomes is the sole purpose of education, advocating for a balance between achieving academic goals and fostering student growth. They highlight the key to creating a lasting impact for students and challenge the notion that coddling and caring for them is the same. Listeners will be intrigued by the discussion on how meditative techniques, introspection, and awareness of one's beliefs can significantly influence the teaching dynamic. Roel also addresses the potential pitfalls of ego in teaching, the importance of seeing students as individuals with unique challenges and aspirations, and the delicate balance of maintaining professional boundaries while cultivating meaningful relationships. This episode is not just for educators. It's a reminder that the learning journey - which never ends - is enriched when both teachers and students show up as whole, interconnected individuals. OVERVIEW > The philosophy behind the Teaching with Heart project and its impact on higher education > The importance of integrating care and love into teaching without compromising on academic rigor > Challenges and opportunities in the advisor-student relationship and how to navigate them > Practical tips for educators to foster a caring classroom environment, even within time constraints > The transformative power of truly listening to and understanding students' needs and aspirations > Reflections on personal growth and the broader implications of Teaching with Heart in the academic world LINKS * Visit https://seg.org/podcasts/episode-219-the-secret-to-succeeding-as-a-teacher-roel-snieder/ for the complete interview transcript and all the links referenced in the show. BIOGRAPHY Roel Snieder holds the W.M. Keck Distinguished Chair of Professional Development Education at the Colorado School of Mines. He received in 1984 a Master's degree in Geophysical Fluid Dynamics from Princeton University and, in 1987, a Ph.D. in seismology from Utrecht University. From 1993-2000, he was a professor of seismology at Utrecht University and served as Dean of the Faculty of Earth Sciences. Roel served on the editorial boards of Geophysical Journal International, Inverse Problems, Reviews of Geophysics, the Journal of the Acoustical Society of America, and the European Journal of Physics. In 2000, he was elected as Fellow of the American Geophysical Union. He is the author of the textbooks "A Guided Tour of Mathematical Methods for the Physical Sciences," "The Art of Being a Scientist," and "The Joy of Science," which is published by Cambridge University Press. In 2011, he was elected as an Honorary Member of the Society of Exploration Geophysicists, and in 2014, he received a research award from the Alexander von Humboldt Foundation. In 2016, Roel received the Beno Gutenberg Medal from the European Geophysical Union and the Outstanding Educator Award from the Society of Exploration Geophysicists. He received in 2020 the Ange Melagro Prize for his outstanding class, Science and Spirituality. In 2023, Roel received the Outstanding Faculty Award from the Colorado School of Mines Board of Trustees. From 2000-2014, he was a firefighter in Genesee Fire Rescue, where he served for two years as Fire Chief. SHOW CREDITS This episode was hosted, edited, and produced by Andrew Geary at TreasureMint. The SEG podcast team comprises Jennifer Cobb, Kathy Gamble, and Ally McGinnis.
26 April 2007 – 14:30 to 15:30
In March 2018 Gudrun had a day available in London when travelling back from the FENICS workshop in Oxford. She contacted a few people working in mathematics at the University College London (ULC) and asked for their time in order to talk about their research. In the end she brought back three episodes for the podcast. This is the second of these conversations. Gudrun talks to Marta Betcke. Marta is associate professor at the UCL Department of Computer Science, member of Centre for Inverse Problems and Centre for Medical Image Computing. She has been in London since 2009. Before that she was a postdoc in the Department of Mathematics at the University of Manchester working on novel X-ray CT scanners for airport baggage screening. This was her entrance into Photoacoustic tomography (PAT), the topic Gudrun and Marta talk about at length in the episode. PAT is a way to see inside objects without destroying them. It makes images of body interiors. There the contrast is due to optical absorption, while the information is carried to the surface of the tissue by ultrasound. This is like measuring the sound of thunder after lightning. Measurements together with mathematics provide ideas about the inside. The technique combines the best of light and sound since good contrast from optical part - though with low resolution - while ultrasound has good resolution but poor contrast (since not enough absorption is going on). In PAT, the measurements are recorded at the surface of the tissue by an array of ultrasound sensors. Each of that only detects the field over a small volume of space, and the measurement continues only for a finite time. In order to form a PAT image, it is necessary to solve an inverse initial value problem by inferring an initial acoustic pressure distribution from measured acoustic time series. In many practical imaging scenarios it is not possible to obtain the full data, or the data may be sub-sampled for faster data acquisition. Then numerical models of wave propagation can be used within the variational image reconstruction framework to find a regularized least-squares solution of an optimization problem. Assuming homogeneous acoustic properties and the absence of acoustic absorption the measured time series can be related to the initial pressure distribution via the spherical mean Radon transform. Integral geometry can be used to derive direct, explicit inversion formulae for certain sensor geometries, such as e.g. spherical arrays. At the moment PAT is predominantly used in preclinical setting, to image tomours and vasculature in small animals. Breast imaging, endoscopic fetus imaging as well as monitoring of perfusion and drug metabolism are subject of intensive ongoing research. The forward problem is related to the absorption of the light and modeled by the wave equation assuming instanteneous absorption and the resulting thearmal expansion. In our case, an optical ultrasound sensor records acoustic waves over time, i.e. providing time series with desired spacial and temporal resolution. Given complete data, then one can mathematically reverse the time direction and find out the original object. Often it is not possible to collect a complete data due to e.g. single sided access to the object as in breast imaging or underlying dynamics happening on a faster rate than one can collect data. In such situations one can formulate the problem in variational framework using regularisation to compensate for the missing data. In particular in subsampling scenario, one would like to use raytracing methods as they scale linearly with the number of sensors. Marta's group is developing flexible acoustic solvers based on ray tracing discretisation of the Green's formulas. They cannot handle reflections but it is approximately correct to assume this to be true as the soundspeed variation is soft tissue is subtle. These solvers can be deployed alongside with stochastic iterative solvers for efficient solution of the variational formulation. Marta went to school in Poland. She finished her education there in a very selected school and loved math due to a great math teacher (which was also her aunt). She decidede to study Computer Sciences, since there she saw more chances on the job market. When moving to Germany her degree was not accepted, so she had to enrol again. This time for Computer Sciences and Engineering at the Hamburg University of Technology. After that she worked on her PhD in the small group of Heinrich Voss there. She had good computing skills and fit in very well. When she finished there she was married and had to solve a two body problem, which brought the couple to Manchester, where a double position was offered. Now both have a permanent position in London. References M. Betcke e.a.: Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography IEEE Transactions on Medical Imaging 37, 1382 - 1393, 2018. F. Rullan & M. Betcke: Hamilton-Green solver for the forward and adjoint problems in photoacoustic tomography archive, 2018. M. Betcke e.a.: On the adjoint operator in photoacoustic tomography Inverse Problems 32, 115012, 2016. doi C. Lutzweiler and D. Razansky: Optoacoustic imaging and tomography - reconstruction approaches and outstanding challenges in image performance and quantification, Sensors 13 7345, 2013. doi: 10.3390/s130607345 Podcasts G. Thäter, K. Page: Embryonic Patterns, Gespräch im Modellansatz Podcast, Folge 161, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. F. Cakoni, G. Thäter: Linear Sampling, Conversation im Modellansatz Podcast, Episode 226, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2019. G. Thäter, R. Aceska: Dynamic Sampling, Gespräch im Modellansatz Podcast, Folge 173, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. S. Fliss, G. Thäter: Transparent Boundaries. Conversation in the Modellansatz Podcast episode 75, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. S. Hollborn: Impedanztomographie. Gespräch mit G. Thäter im Modellansatz Podcast, Folge 68, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2015. M. Kray, G. Thäter: Splitting Waves. Conversation in the Modellansatz Podcast episode 62, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. F. Sayas, G. Thäter: Acoustic scattering. Conversation in the Modellansatz Podcast episode 58, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015.
In March 2018 Gudrun had a day available in London when travelling back from the FENICS workshop in Oxford. She contacted a few people working in mathematics at the University College London (ULC) and asked for their time in order to talk about their research. In the end she brought back three episodes for the podcast. This is the second of these conversations. Gudrun talks to Marta Betcke. Marta is associate professor at the UCL Department of Computer Science, member of Centre for Inverse Problems and Centre for Medical Image Computing. She has been in London since 2009. Before that she was a postdoc in the Department of Mathematics at the University of Manchester working on novel X-ray CT scanners for airport baggage screening. This was her entrance into Photoacoustic tomography (PAT), the topic Gudrun and Marta talk about at length in the episode. PAT is a way to see inside objects without destroying them. It makes images of body interiors. There the contrast is due to optical absorption, while the information is carried to the surface of the tissue by ultrasound. This is like measuring the sound of thunder after lightning. Measurements together with mathematics provide ideas about the inside. The technique combines the best of light and sound since good contrast from optical part - though with low resolution - while ultrasound has good resolution but poor contrast (since not enough absorption is going on). In PAT, the measurements are recorded at the surface of the tissue by an array of ultrasound sensors. Each of that only detects the field over a small volume of space, and the measurement continues only for a finite time. In order to form a PAT image, it is necessary to solve an inverse initial value problem by inferring an initial acoustic pressure distribution from measured acoustic time series. In many practical imaging scenarios it is not possible to obtain the full data, or the data may be sub-sampled for faster data acquisition. Then numerical models of wave propagation can be used within the variational image reconstruction framework to find a regularized least-squares solution of an optimization problem. Assuming homogeneous acoustic properties and the absence of acoustic absorption the measured time series can be related to the initial pressure distribution via the spherical mean Radon transform. Integral geometry can be used to derive direct, explicit inversion formulae for certain sensor geometries, such as e.g. spherical arrays. At the moment PAT is predominantly used in preclinical setting, to image tomours and vasculature in small animals. Breast imaging, endoscopic fetus imaging as well as monitoring of perfusion and drug metabolism are subject of intensive ongoing research. The forward problem is related to the absorption of the light and modeled by the wave equation assuming instanteneous absorption and the resulting thearmal expansion. In our case, an optical ultrasound sensor records acoustic waves over time, i.e. providing time series with desired spacial and temporal resolution. Given complete data, then one can mathematically reverse the time direction and find out the original object. Often it is not possible to collect a complete data due to e.g. single sided access to the object as in breast imaging or underlying dynamics happening on a faster rate than one can collect data. In such situations one can formulate the problem in variational framework using regularisation to compensate for the missing data. In particular in subsampling scenario, one would like to use raytracing methods as they scale linearly with the number of sensors. Marta's group is developing flexible acoustic solvers based on ray tracing discretisation of the Green's formulas. They cannot handle reflections but it is approximately correct to assume this to be true as the soundspeed variation is soft tissue is subtle. These solvers can be deployed alongside with stochastic iterative solvers for efficient solution of the variational formulation. Marta went to school in Poland. She finished her education there in a very selected school and loved math due to a great math teacher (which was also her aunt). She decidede to study Computer Sciences, since there she saw more chances on the job market. When moving to Germany her degree was not accepted, so she had to enrol again. This time for Computer Sciences and Engineering at the Hamburg University of Technology. After that she worked on her PhD in the small group of Heinrich Voss there. She had good computing skills and fit in very well. When she finished there she was married and had to solve a two body problem, which brought the couple to Manchester, where a double position was offered. Now both have a permanent position in London. References M. Betcke e.a.: Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography IEEE Transactions on Medical Imaging 37, 1382 - 1393, 2018. F. Rullan & M. Betcke: Hamilton-Green solver for the forward and adjoint problems in photoacoustic tomography archive, 2018. M. Betcke e.a.: On the adjoint operator in photoacoustic tomography Inverse Problems 32, 115012, 2016. doi C. Lutzweiler and D. Razansky: Optoacoustic imaging and tomography - reconstruction approaches and outstanding challenges in image performance and quantification, Sensors 13 7345, 2013. doi: 10.3390/s130607345 Podcasts G. Thäter, K. Page: Embryonic Patterns, Gespräch im Modellansatz Podcast, Folge 161, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. F. Cakoni, G. Thäter: Linear Sampling, Conversation im Modellansatz Podcast, Episode 226, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2019. G. Thäter, R. Aceska: Dynamic Sampling, Gespräch im Modellansatz Podcast, Folge 173, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. S. Fliss, G. Thäter: Transparent Boundaries. Conversation in the Modellansatz Podcast episode 75, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. S. Hollborn: Impedanztomographie. Gespräch mit G. Thäter im Modellansatz Podcast, Folge 68, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2015. M. Kray, G. Thäter: Splitting Waves. Conversation in the Modellansatz Podcast episode 62, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. F. Sayas, G. Thäter: Acoustic scattering. Conversation in the Modellansatz Podcast episode 58, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015.
In den nächsten Wochen bis zum 20.2.2020 möchte Anna Hein, Studentin der Wissenschaftskommunikation am KIT, eine Studie im Rahmen ihrer Masterarbeit über den Podcast Modellansatz durchführen. Dazu möchte sie gerne einige Interviews mit Ihnen, den Hörerinnen und Hörern des Podcast Modellansatz führen, um herauszufinden, wer den Podcast hört und wie und wofür er genutzt wird. Die Interviews werden anonymisiert und werden jeweils circa 15 Minuten in Anspruch nehmen. Für die Teilnahme an der Studie können Sie sich bis zum 20.2.2020 unter der Emailadresse studie.modellansatz@web.de bei Anna Hein melden. Wir würden uns sehr freuen, wenn sich viele Interessenten melden würden. In the coming weeks until February 20, 2020, Anna Hein, student of science communication at KIT, intends to conduct a study on the Modellansatz Podcast within her master's thesis. For this purpose, she would like to conduct some interviews with you, the listeners of the Modellansatz Podcast, to find out who listens to the podcast and how and for what purpose it is used. The interviews will be anonymous and will take about 15 minutes each. To participate in the study, you can register with Anna Hein until 20.2.2020 at studie.modellansatz@web.de . We would be very pleased if many interested parties would contact us. This is the first of three conversation recorded Conference on mathematics of wave phenomena 23-27 July 2018 in Karlsruhe. Gudrun talked to Fioralba Cakoni about the Linear Sampling Method and Scattering. The linear sampling method is a method to reconstruct the shape of an obstacle without a priori knowledge of either the physical properties or the number of disconnected components of the scatterer. The principal problem is to detect objects inside an object without seeing it with our eyes. So we send waves of a certain frequency range into an object and then measure the response on the surface of the body. The waves can be absorbed, reflected and scattered inside the body. From this answer we would like to detect if there is something like a tumor inside the body and if yes where. Or to be more precise what is the shape of the tumor. Since the problem is non-linear and ill posed this is a difficult question and needs severyl mathematical steps on the analytical as well as the numerical side. In 1996 Colton and Kirsch (reference below) proposed a new method for the obstacle reconstruction problem in inverse scattering which is today known as the linear sampling method. It is a method to solve the above stated problem, which scientists call an inverse scattering problem. The method of linear sampling combines the answers to lots of frequencies but stays linear. So the problem in itself is not approximated but the interpretation of the response is. The central idea is to invert a bounded operator which is constructed with the help of the integral over the boundary of the body. Fioralba got her Diploma (honor’s program) and her Master's in Mathematics at the University of Tirana. For her Ph.D. she worked with George Dassios from the University of Patras but stayed at the University of Tirana. After that she worked with Wolfgang Wendland at the University of Stuttgart as Alexander von Humboldt Research Fellow. During her second year in Stuttgart she got a position at the University of Delaware in Newark. Since 2015 she has been Professor at Rutgers University. She works at the Campus in Piscataway near New Brunswick (New Jersey). References F. Cakoni, D. Colton and H. Haddar, Inverse Scattering Theory and Transmission Eigenvalues, CBMS-NSF Regional Conference Series in Applied Mathematics, 88, SIAM Publications, 2016. F. Cakoni, D. Colton, A Qualitative Approach to Inverse Scattering Theory, Springer, Applied Mathematical Series, Vol. 188, 2014. T. Arens: Why linear sampling works, Inverse Problems 20 163-173, 2003. https://doi.org/10.1088/0266-5611/20/1/010 A. Kirsch: Characterization of the shape of a scattering obstacle using the spectral data of the far field operator, Inverse Problems 14 1489-512, 1998 D. Colton, A. Kirsch: A simple method for solving inverse scattering problems in the resonance region, Inverse Problems 12 383-93, 1996. Podcasts S. Fliss, G. Thäter: Transparent Boundaries. Conversation in the Modellansatz Podcast episode 75, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. M. Kray, G. Thäter: Splitting Waves. Conversation in the Modellansatz Podcast episode 62, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. F. Sayas, G. Thäter: Acoustic scattering. Conversation in the Modellansatz Podcast episode 58, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015.
In den nächsten Wochen bis zum 20.2.2020 möchte Anna Hein, Studentin der Wissenschaftskommunikation am KIT, eine Studie im Rahmen ihrer Masterarbeit über den Podcast Modellansatz durchführen. Dazu möchte sie gerne einige Interviews mit Ihnen, den Hörerinnen und Hörern des Podcast Modellansatz führen, um herauszufinden, wer den Podcast hört und wie und wofür er genutzt wird. Die Interviews werden anonymisiert und werden jeweils circa 15 Minuten in Anspruch nehmen. Für die Teilnahme an der Studie können Sie sich bis zum 20.2.2020 unter der Emailadresse studie.modellansatz@web.de bei Anna Hein melden. Wir würden uns sehr freuen, wenn sich viele Interessenten melden würden. In the coming weeks until February 20, 2020, Anna Hein, student of science communication at KIT, intends to conduct a study on the Modellansatz Podcast within her master's thesis. For this purpose, she would like to conduct some interviews with you, the listeners of the Modellansatz Podcast, to find out who listens to the podcast and how and for what purpose it is used. The interviews will be anonymous and will take about 15 minutes each. To participate in the study, you can register with Anna Hein until 20.2.2020 at studie.modellansatz@web.de . We would be very pleased if many interested parties would contact us. This is the first of three conversation recorded Conference on mathematics of wave phenomena 23-27 July 2018 in Karlsruhe. Gudrun talked to Fioralba Cakoni about the Linear Sampling Method and Scattering. The linear sampling method is a method to reconstruct the shape of an obstacle without a priori knowledge of either the physical properties or the number of disconnected components of the scatterer. The principal problem is to detect objects inside an object without seeing it with our eyes. So we send waves of a certain frequency range into an object and then measure the response on the surface of the body. The waves can be absorbed, reflected and scattered inside the body. From this answer we would like to detect if there is something like a tumor inside the body and if yes where. Or to be more precise what is the shape of the tumor. Since the problem is non-linear and ill posed this is a difficult question and needs severyl mathematical steps on the analytical as well as the numerical side. In 1996 Colton and Kirsch (reference below) proposed a new method for the obstacle reconstruction problem in inverse scattering which is today known as the linear sampling method. It is a method to solve the above stated problem, which scientists call an inverse scattering problem. The method of linear sampling combines the answers to lots of frequencies but stays linear. So the problem in itself is not approximated but the interpretation of the response is. The central idea is to invert a bounded operator which is constructed with the help of the integral over the boundary of the body. Fioralba got her Diploma (honor’s program) and her Master's in Mathematics at the University of Tirana. For her Ph.D. she worked with George Dassios from the University of Patras but stayed at the University of Tirana. After that she worked with Wolfgang Wendland at the University of Stuttgart as Alexander von Humboldt Research Fellow. During her second year in Stuttgart she got a position at the University of Delaware in Newark. Since 2015 she has been Professor at Rutgers University. She works at the Campus in Piscataway near New Brunswick (New Jersey). References F. Cakoni, D. Colton and H. Haddar, Inverse Scattering Theory and Transmission Eigenvalues, CBMS-NSF Regional Conference Series in Applied Mathematics, 88, SIAM Publications, 2016. F. Cakoni, D. Colton, A Qualitative Approach to Inverse Scattering Theory, Springer, Applied Mathematical Series, Vol. 188, 2014. T. Arens: Why linear sampling works, Inverse Problems 20 163-173, 2003. https://doi.org/10.1088/0266-5611/20/1/010 A. Kirsch: Characterization of the shape of a scattering obstacle using the spectral data of the far field operator, Inverse Problems 14 1489-512, 1998 D. Colton, A. Kirsch: A simple method for solving inverse scattering problems in the resonance region, Inverse Problems 12 383-93, 1996. Podcasts S. Fliss, G. Thäter: Transparent Boundaries. Conversation in the Modellansatz Podcast episode 75, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. M. Kray, G. Thäter: Splitting Waves. Conversation in the Modellansatz Podcast episode 62, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015. F. Sayas, G. Thäter: Acoustic scattering. Conversation in the Modellansatz Podcast episode 58, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2015.
Julian Edward is a Professor and the Director of the Learning Assistant Program at Florida International University¬. He received his Ph.D. at the Massachusetts Institute of Technology under the supervision of Richard Melrose. His main area of research is analysis and partial differential equations. He has done research in Control Theory, Inverse Problems, Spectral and Scattering theory for Schrodinger operators as well as work in voting theory.We talk about his journey through grad school and being hired as a professor, what it's like being the chair of the mathematics department, his research in control theory and voting theory,His website can be found here:http://faculty.fiu.edu/~edwardj/We'd like to thank Julian for being on our show "Meet a Mathematician" and for sharing his stories and perspective with us!www.sensemakesmath.comPODCAST: http://sensemakesmath.buzzsprout.com/TWITTER: @SenseMakesMathPATREON: https://www.patreon.com/sensemakesmathFACEBOOK: https://www.facebook.com/SenseMakesMathSTORE: https://sensemakesmath.storenvy.comSupport the show (https://www.patreon.com/sensemakesmath)
Can we make objects invisible? Professor Gunther Uhlmann explores inverse problems, and the progress scientists are making to achieve invisibility.
This is the last of four conversation Gudrun had during the British Applied Mathematics Colloquium which took place 5th – 8th April 2016 in Oxford. Andrea Bertozzi from the University of California in Los Angeles (UCLA) held a public lecture on The Mathematics of Crime. She has been Professor of Mathematics at UCLA since 2003 and Betsy Wood Knapp Chair for Innovation and Creativity (since 2012). From 1995-2004 she worked mostly at Duke University first as Associate Professor of Mathematics and then as Professor of Mathematics and Physics. As an undergraduate at Princeton University she studied physics and astronomy alongside her major in mathematics and went through a Princeton PhD-program. For her thesis she worked in applied analysis and studied fluid flow. As postdoc she worked with Peter Constantin at the University of Chicago (1991-1995) on global regularity for vortex patches. But even more importantly, this was the moment when she found research problems that needed knowledge about PDEs and flow but in addition both numerical analysis and scientific computing. She found out that she really likes to collaborate with very different specialists. Today hardwork can largely be carried out on a desktop but occasionally clusters or supercomputers are necessary. The initial request to work on Mathematics in crime came from a colleague, the social scientist Jeffrey Brantingham. He works in Anthropology at UCLA and had well established contacts with the police in LA. He was looking for mathematical input on some of his problems and raised that issue with Andrea Bertozzi. Her postdoc George Mohler came up with the idea to adapt an earthquake model after a discussion with Frederic Paik Schoenberg, a world expert in that field working at UCLA. The idea is to model crimes of opportunity as being triggered by crimes that already happend. So the likelihood of new crimes can be predicted as an excitation in space and time like the shock of an earthquake. Of course, here statistical models are necessary which say how the excitement is distributed and decays in space and time. Mathematically this is a self-exciting point process. The traditional Poisson process model has a single parameter and thus, no memory - i.e. no connections to other events can be modelled. The Hawkes process builds on the Poisson process as background noise but adds new events which then are triggering events according to an excitation rate and the exponential decay of excitation over time. This is a memory effect based on actual events (not only on a likelihood) and a three parameter model. It is not too difficult to process field data, fit data to that model and make an extrapolation in time. Meanwhile the results of that idea work really well in the field. Results of field trials both in the UK and US have just been published and there is a commercial product available providing services to the police. In addition to coming up with useful ideas and having an interdisciplinary group of people committed to make them work it was necessery to find funding in order to support students to work on that topic. The first grant came from the National Science Foundation and from this time on the group included George Tita (UC Irvine) a criminology expert in LA-Gangs and Lincoln Chayes as another mathematician in the team. The practical implementation of this crime prevention method for the police is as follows: Before the policemen go out on a shift they ususally meet to divide their teams over the area they are serving. The teams take the crime prediction for that shift which is calculated by the computer model on the basis of whatever data is available up to shift. According to expected spots of crimes they especially assign teams to monitor those areas more closely. After introducing this method in the police work in Santa Cruz (California) police observed a significant reduction of 27% in crime. Of course this is a wonderful success story. Another success story involves the career development of the students and postdocs who now have permanent positions. Since this was the first group in the US to bring mathematics to police work this opened a lot of doors for young people involved. Another interesting topic in the context of Mathematics and crime are gang crime data. As for the the crime prediction model the attack of one gang on a rival gang usually triggers another event soon afterwards. A well chosen group of undergraduates already is mathematically educated enough to study the temporary distribution of gang related crime in LA with 30 street gangs and a complex net of enemies. We are speaking about hundreds of crimes in one year related to the activity of gangs. The mathematical tool which proved to be useful was a maximum liklihood penalization model again for the Hawkes process applied on the expected retaliatory behaviour. A more complex problem, which was treated in a PhD-thesis, is to single out gangs which would be probably responsable for certain crimes. This means to solve the inverse problem: We know the time and the crime and want to find out who did it. The result was published in Inverse Problems 2011. The tool was a variational model with an energy which is related to the data. The missing information is guessed and then put into the energy . In finding the best guess related to the chosen energy model a probable candidate for the crime is found. For a small number of unsolved crimes one can just go through all possible combinations. For hundreds or even several hundreds of unsolved crimes - all combinations cannot be handled. We make it easier by increasing the number of choices and formulate a continuous instead of the discrete problem, for which the optimization works with a standard gradient descent algorithm. A third topic and a third tool is Compressed sensing. It looks at sparsitiy in data like the probability distribution for crime in different parts of the city. Usually the crime rate is high in certain areas of a city and very low in others. For these sharp changes one needs different methods since we have to allow for jumps. Here the total variation enters the model as the -norm of the gradient. It promotes sparsity of edges in the solution. Before coming up with this concept it was necessary to cross-validate quite a number of times, which is computational very expensive. So instead of in hours the result is obtained in a couple minutes now. When Andrea Bertozzi was a young child she spent a lot of Sundays in the Science museum in Boston and wanted to become a scientist when grown up. The only problem was, that she could not decide which science would be the best choice since she liked everything in the museum. Today she says having chosen applied mathematics indeed she can do all science since mathematics works as a connector between sciences and opens a lot of doors. References Press coverage of Crime prevention collected Website of Mathematical and Simulation Modeling of Crime Examples for work of undergraduates M. Allenby, e.a.: A Point Process Model for Simulating Gang-on-Gang Violence, Project Report, 2010. K. Louie: Statistical Modeling of Gang Violence in Los Angeles, talk at AMS Joint meetings San Francisco, AMS Session on Mathematics in the Social Sciences, 2010] Publications of A. Bertozzi and co-workers on Crime prevention G.O. Mohler e.a.: Randomized controlled field trials of predictive policing, J. Am. Stat. Assoc., 111(512), 1399-1411, 2015. J. T. Woodworth e.a.: Nonlocal Crime Density Estimation Incorporating Housing Information, Phil. Trans. Roy. Soc. A, 372(2028), 20130403, 2014. J. Zipkin, M. B. Short & A. L. Bertozzi: Cops on the dots in a mathematical model of urban crime and police response, Discrete and Continuous Dynamical Systems B, 19(5), pp. 1479-1506, 2014. H. Hu e.a.: A Method Based on Total Variation for Network Modularity Optimization using the MBO Scheme, SIAM J. Appl. Math., 73(6), pp. 2224-2246, 2013. L.M. Smith e.a.: Adaptation of an Ecological Territorial Model to Street Gang Spatial Patterns in Los Angeles Discrete and Continuous Dynamical Systems A, 32(9), pp. 3223 - 3244, 2012. G. Mohler e.a.. (2011): Self- exciting point process modeling of crime, Journal of the American Statistical Association, 106(493):100–108, 2011. A. Stomakhin, M. Short, and A. Bertozzi: Reconstruction of missing data in social networks based on temporal patterns of interactions. Inverse Problems, 27, 2011. N. Rodriguez & A. Bertozzi: Local Existence and Uniqueness of Solutions to a PDE model for Criminal Behavior , M3AS, special issue on Mathematics and Complexity in Human and Life Sciences, Vol. 20, Issue supp01, pp. 1425-1457, 2010. Related Podcasts AMS - Mathematical Moments Podcast: MM97 - Forecasting Crime British Applied Mathematics Colloquium 2016 Special J.Dodd: Crop Growth, Conversation with G. Thäter in the Modellansatz Podcast episode 89, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2016. http://modellansatz.de/crop-growth H. Wilson: Viscoelastic Fluids, Conversation with G. Thäter in the Modellansatz Podcast episode 92, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2016. http://modellansatz.de/viscoelastic-fluids A. Hosoi: Robots, Conversation with G. Thäter in the Modellansatz Podcast, episode 108, Department for Mathematics, Karlsruhe Institute of Technologie (KIT), 2016. http://modellansatz.de/robot A. Bertozzi: Crime Prevention, Conversation with G. Thäter in the Modellansatz Podcast, episode 109, Department for Mathematics, Karlsruhe Institute of Technologie (KIT), 2016. http://modellansatz.de/crime-prevention
This is the last of four conversation Gudrun had during the British Applied Mathematics Colloquium which took place 5th – 8th April 2016 in Oxford. Andrea Bertozzi from the University of California in Los Angeles (UCLA) held a public lecture on The Mathematics of Crime. She has been Professor of Mathematics at UCLA since 2003 and Betsy Wood Knapp Chair for Innovation and Creativity (since 2012). From 1995-2004 she worked mostly at Duke University first as Associate Professor of Mathematics and then as Professor of Mathematics and Physics. As an undergraduate at Princeton University she studied physics and astronomy alongside her major in mathematics and went through a Princeton PhD-program. For her thesis she worked in applied analysis and studied fluid flow. As postdoc she worked with Peter Constantin at the University of Chicago (1991-1995) on global regularity for vortex patches. But even more importantly, this was the moment when she found research problems that needed knowledge about PDEs and flow but in addition both numerical analysis and scientific computing. She found out that she really likes to collaborate with very different specialists. Today hardwork can largely be carried out on a desktop but occasionally clusters or supercomputers are necessary. The initial request to work on Mathematics in crime came from a colleague, the social scientist Jeffrey Brantingham. He works in Anthropology at UCLA and had well established contacts with the police in LA. He was looking for mathematical input on some of his problems and raised that issue with Andrea Bertozzi. Her postdoc George Mohler came up with the idea to adapt an earthquake model after a discussion with Frederic Paik Schoenberg, a world expert in that field working at UCLA. The idea is to model crimes of opportunity as being triggered by crimes that already happend. So the likelihood of new crimes can be predicted as an excitation in space and time like the shock of an earthquake. Of course, here statistical models are necessary which say how the excitement is distributed and decays in space and time. Mathematically this is a self-exciting point process. The traditional Poisson process model has a single parameter and thus, no memory - i.e. no connections to other events can be modelled. The Hawkes process builds on the Poisson process as background noise but adds new events which then are triggering events according to an excitation rate and the exponential decay of excitation over time. This is a memory effect based on actual events (not only on a likelihood) and a three parameter model. It is not too difficult to process field data, fit data to that model and make an extrapolation in time. Meanwhile the results of that idea work really well in the field. Results of field trials both in the UK and US have just been published and there is a commercial product available providing services to the police. In addition to coming up with useful ideas and having an interdisciplinary group of people committed to make them work it was necessery to find funding in order to support students to work on that topic. The first grant came from the National Science Foundation and from this time on the group included George Tita (UC Irvine) a criminology expert in LA-Gangs and Lincoln Chayes as another mathematician in the team. The practical implementation of this crime prevention method for the police is as follows: Before the policemen go out on a shift they ususally meet to divide their teams over the area they are serving. The teams take the crime prediction for that shift which is calculated by the computer model on the basis of whatever data is available up to shift. According to expected spots of crimes they especially assign teams to monitor those areas more closely. After introducing this method in the police work in Santa Cruz (California) police observed a significant reduction of 27% in crime. Of course this is a wonderful success story. Another success story involves the career development of the students and postdocs who now have permanent positions. Since this was the first group in the US to bring mathematics to police work this opened a lot of doors for young people involved. Another interesting topic in the context of Mathematics and crime are gang crime data. As for the the crime prediction model the attack of one gang on a rival gang usually triggers another event soon afterwards. A well chosen group of undergraduates already is mathematically educated enough to study the temporary distribution of gang related crime in LA with 30 street gangs and a complex net of enemies. We are speaking about hundreds of crimes in one year related to the activity of gangs. The mathematical tool which proved to be useful was a maximum liklihood penalization model again for the Hawkes process applied on the expected retaliatory behaviour. A more complex problem, which was treated in a PhD-thesis, is to single out gangs which would be probably responsable for certain crimes. This means to solve the inverse problem: We know the time and the crime and want to find out who did it. The result was published in Inverse Problems 2011. The tool was a variational model with an energy which is related to the data. The missing information is guessed and then put into the energy . In finding the best guess related to the chosen energy model a probable candidate for the crime is found. For a small number of unsolved crimes one can just go through all possible combinations. For hundreds or even several hundreds of unsolved crimes - all combinations cannot be handled. We make it easier by increasing the number of choices and formulate a continuous instead of the discrete problem, for which the optimization works with a standard gradient descent algorithm. A third topic and a third tool is Compressed sensing. It looks at sparsitiy in data like the probability distribution for crime in different parts of the city. Usually the crime rate is high in certain areas of a city and very low in others. For these sharp changes one needs different methods since we have to allow for jumps. Here the total variation enters the model as the -norm of the gradient. It promotes sparsity of edges in the solution. Before coming up with this concept it was necessary to cross-validate quite a number of times, which is computational very expensive. So instead of in hours the result is obtained in a couple minutes now. When Andrea Bertozzi was a young child she spent a lot of Sundays in the Science museum in Boston and wanted to become a scientist when grown up. The only problem was, that she could not decide which science would be the best choice since she liked everything in the museum. Today she says having chosen applied mathematics indeed she can do all science since mathematics works as a connector between sciences and opens a lot of doors. References Press coverage of Crime prevention collected Website of Mathematical and Simulation Modeling of Crime Examples for work of undergraduates M. Allenby, e.a.: A Point Process Model for Simulating Gang-on-Gang Violence, Project Report, 2010. K. Louie: Statistical Modeling of Gang Violence in Los Angeles, talk at AMS Joint meetings San Francisco, AMS Session on Mathematics in the Social Sciences, 2010] Publications of A. Bertozzi and co-workers on Crime prevention G.O. Mohler e.a.: Randomized controlled field trials of predictive policing, J. Am. Stat. Assoc., 111(512), 1399-1411, 2015. J. T. Woodworth e.a.: Nonlocal Crime Density Estimation Incorporating Housing Information, Phil. Trans. Roy. Soc. A, 372(2028), 20130403, 2014. J. Zipkin, M. B. Short & A. L. Bertozzi: Cops on the dots in a mathematical model of urban crime and police response, Discrete and Continuous Dynamical Systems B, 19(5), pp. 1479-1506, 2014. H. Hu e.a.: A Method Based on Total Variation for Network Modularity Optimization using the MBO Scheme, SIAM J. Appl. Math., 73(6), pp. 2224-2246, 2013. L.M. Smith e.a.: Adaptation of an Ecological Territorial Model to Street Gang Spatial Patterns in Los Angeles Discrete and Continuous Dynamical Systems A, 32(9), pp. 3223 - 3244, 2012. G. Mohler e.a.. (2011): Self- exciting point process modeling of crime, Journal of the American Statistical Association, 106(493):100–108, 2011. A. Stomakhin, M. Short, and A. Bertozzi: Reconstruction of missing data in social networks based on temporal patterns of interactions. Inverse Problems, 27, 2011. N. Rodriguez & A. Bertozzi: Local Existence and Uniqueness of Solutions to a PDE model for Criminal Behavior , M3AS, special issue on Mathematics and Complexity in Human and Life Sciences, Vol. 20, Issue supp01, pp. 1425-1457, 2010. Related Podcasts AMS - Mathematical Moments Podcast: MM97 - Forecasting Crime British Applied Mathematics Colloquium 2016 Special J.Dodd: Crop Growth, Conversation with G. Thäter in the Modellansatz Podcast episode 89, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2016. http://modellansatz.de/crop-growth H. Wilson: Viscoelastic Fluids, Conversation with G. Thäter in the Modellansatz Podcast episode 92, Department of Mathematics, Karlsruhe Institute of Technology (KIT), 2016. http://modellansatz.de/viscoelastic-fluids A. Hosoi: Robots, Conversation with G. Thäter in the Modellansatz Podcast, episode 108, Department for Mathematics, Karlsruhe Institute of Technologie (KIT), 2016. http://modellansatz.de/robot A. Bertozzi: Crime Prevention, Conversation with G. Thäter in the Modellansatz Podcast, episode 109, Department for Mathematics, Karlsruhe Institute of Technologie (KIT), 2016. http://modellansatz.de/crime-prevention
de Hoop, M (Rice University) Friday 8th April 2016 - 16:00 to 17:00
Capdeville, Y [CNRS (Centre national de la recherche scientifique), Université de Nantes] Wednesday 13th April 2016 - 14:30 to 15:30
Mathematik mit Kunst und Design erklären - das war ein Ziel des Cooking Math-Projekts. Robert Winkler forscht an der Fakultät für Mathematik zu numerischen Verfahren für schlecht gestellte Probleme. Das hilft z.B. Elektrische Impedanztomographie genauer und schneller zu machen. Seine Teilnahme am Cooking Math Projektes hat uns zum jetzigen Zeitpunkt zusammengeführt. Die Aufgabenstellung der Elektrischen Impedanztomographie ist es, aus Messungen auf der Oberfläche eines Körpers Rückschlüsse auf die Zusammensetzung im Inneren zu ziehen. Dazu dient bei der Elektrische Impedanztomographie die elektrische Leitfähigkeit im Innern, die Auswirkungen auf gemessene elektrische Potentiale an der Körperoberfläche hat. Aus physikalischen Zusammenhängen (hier Ohmsches Gesetz und Kirchhoffsche Regeln) lassen sich partielle Differentialgleichungen herleiten, die aus der Leitung im Innern die Oberflächenpotentiale berechenbar machen. Das nennt man Vorwärtsproblem. In der Praxis braucht man aber genau die andere Richtung - das sogenannte inverse Problem - denn man hat die Werte auf dem Rand gemessen und will unter den gleichen physikalischen Annahmen auf den Ablauf im Inneren schließen. Der Zusammenhang, der so modellhaft zwischen Leitfähigkeit und Potential am Rand entsteht, ist hochgradig nichtlinear. Außerdem ist er instabil, das heißt kleine Messfehler können dramatische Auswirkungen auf die Bestimmung der Leitfähigkeit haben. Daher müssen bei der numerischen Bearbeitung Verfahren gefunden werden, die die partielle Differentialgleichung numerisch lösen und dabei diese Nichtlinearität stabil behandeln können. Etabliert und sehr effektiv ist dabei das Newtonverfahren. Es ist weithin bekannt zur Nullstellensuche bei Funktionen von einer Variablen. Die grundlegende Idee ist, dass man ausgehend von einem Punkt in der Nähe der Nullstelle den Tangenten an der Funktion folgt um sich schrittweise der Nullstelle zu nähern. Durch die Information, die in der Tangentenrichtung verschlüsselt ist, entsteht so ein Verfahren zweiter Ordnung, was in der Praxis heißt, dass sich nach kurzer Zeit in jedem Schritt die Zahl der gültigen Stellen verdoppelt. Großer Nachteil ist, dass das nur dann funktioniert, wenn man nahe genug an der Nullstelle startet (dh. in der Regel braucht man zuerst ein Verfahren, das schon eine gute erste Schätzung für die Nullstelle liefert). Außerdem gibt es Probleme, wenn die Nullstelle nicht einfach ist. Wenn man das Newtonverfahren zum finden von Optimalstellen nutzt (dort wo die Ableitung eine Nullstelle hat), kann es natürlich nur lokale Minima/Maxima finden und auch nur dasjenige, das am nächsten vom Startwert liegt. Im Kontext der inversen Probleme wird das Newtonverfahren auch eingesetzt. Hier muss natürlich vorher eine geeignete Verallgemeinerung gefunden werden, die so wie die Ableitungen im eindimensionalen Fall eine Linearisierung der Funktion in einer (kleinen) Umgebung des Punktes sind. Der Kontext, in dem das recht gut möglich ist, ist die schwache Formulierung der partiellen Differentialgleichung. Der passende Begriff ist dann die Fréchet-Ableitung. Betrachtet man das Problem mathematisch in einem Raum mit Skalarprodukt (Hilbertraum), kann die Linearisierung mit dem Verfahren der konjugierten Gradienten behandelt werden. Dieses Verfahren findet besonders schnell eine gute Näherung an die gesuchte Lösung, indem es sich Eigenschaften des Skalarprodukts zunutze macht und die aktuelle Näherung schrittweise in besonders "effektive" Richtungen verbessert. Um das lineare Problem stabiler zu machen, nutzt man Regularisierungen und geht von vornherein davon aus, dass man durch Fehler in den Daten und im Modell ohnehin in der Genauigkeit eingeschränkt ist und in der numerischen Lösung nicht versuchen sollte, mehr Genauigkeit vorzutäuschen. Eine typische Regularisierung bei der Elektrische Impedanztomographie ist die Erwartung, dass die Leitfähigkeit stückweise konstant ist, weil jedes Material eine zugehörige Konstante besitzt. Im zugehörigen Cooking Math-Projekt soll der Modellerierungs- und Lösungsfindungsprozess visualisiert werden. Eine Idee hierfür ist das Spiel "Topfschlagen". Literatur und weiterführende Informationen R. Winkler, A. Rieder: Model-Aware Newton-Type Inversion Scheme for Electrical Impedance Tomography, Preprint 14/04 am Institut für Wissenschaftliches Rechnen und Mathematische Modellbildung, KIT, 2014. (Eingereicht zur Veröffentlichung in Inverse Problems 31, (2015) 045009). O. Scherzer: Handbook of Mathematical Methods in Imaging, Springer Verlag, ISBN 978-0-387-92919-4, 2011. Podcasts S. Hollborn: Impedanztomographie, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 68, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2015. http://modellansatz.de/impedanztomographie J. Enders, C. Spatschek: Cooking Math, Gespräch mit G. Thäter und S. Ritterbusch im Modellansatz Podcast, Folge 80, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2016. http://modellansatz.de/cooking-math J. Eilinghoff: Splitting, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 81, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2016. http://modellansatz.de/splitting P. Krämer: Zeitintegration, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 82, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2016. http://modellansatz.de/zeitintegration D. Hipp: Dynamische Randbedingungen, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 83, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2016. http://modellansatz.de/dynamische-randbedingungen
If we are interested in the propagation of waves around a small region of interest, like e.g. an obstacle inside a very big ("unbounded") domain, one way to bring such problems to the computer and solve them numerically is to cut that unbounded domain to a bounded domain. But to have a well-posed problem we have to prescribe boundary conditions on the so-called artificial boundary, which are not inherent in our original problem. This is a classical problem which is not only connected to wave phenomena. Sonia Fliss is interested in so-called transparent boundary conditions. These are the boundary conditions on the artificial boundaries with just the right properties. There are several classical methods like perfectly matched layers (PML) around the region of interest. They are built to absorb incoming waves (complex stretching of space variable). But unfortunately this does not work for non-homogeneous media. Traditionally, also boundary integral equations were used to construct transparent boundary conditions. But in general, this is not possible for anisotropic media (or heterogenous media, e.g. having periodic properties). The main idea in the work of Sonia Fliss is quite simple: She surrounds the region of interest with half spaces (three or more). Then, the solutions in each of these half spaces are determined by Fourier transform (or Floquet waves for periodic media, respectively). The difficulty is that in the overlap of the different half spaces the representations of the solutions have to coincide. Sonia Fliss proposes a method which ensures that this is true (eventually under certain compatibility conditions). The chosen number of half spaces does not change the method very much. The idea is charmingly simple, but the proof that these solutions exist and have the right properties is more involved. She is still working on making the proofs more easy to understand and apply. It is a fun fact, that complex media were the starting point for the idea, and only afterwards it became clear that it also works perfectly well for homogeneous (i.e. much less complex) media. One might consider this to be very theoretical result, but they lead to numerical simulations which match our expectations and are quite impressive and impossible without knowing the right transparent boundary conditions. Sonia Fliss is still very fascinated by the many open theoretical questions. At the moment she is working at Ecole Nationale Supérieure des Techniques avancées (ENSTA) near Paris as Maitre de conférence. Literature and additional material C. Besse, J. Coatleven, S. Fliss, I. Lacroix-Violet, K. Ramdani: Transparent boundary conditions for locally perturbed infinite hexagonal periodic media, arXiv preprint arXiv:1205.5345, 2012. S. Fliss, P. Joly: Exact boundary conditions for time-harmonic wave propagation in locally perturbed periodic media, Applied Numerical Mathematics 59.9: 2155-2178, 2009. L. Bourgeois, S. Fliss: On the identification of defects in a periodic waveguide from far field data, Inverse Problems 30.9: 095004, 2014.
If we are interested in the propagation of waves around a small region of interest, like e.g. an obstacle inside a very big ("unbounded") domain, one way to bring such problems to the computer and solve them numerically is to cut that unbounded domain to a bounded domain. But to have a well-posed problem we have to prescribe boundary conditions on the so-called artificial boundary, which are not inherent in our original problem. This is a classical problem which is not only connected to wave phenomena. Sonia Fliss is interested in so-called transparent boundary conditions. These are the boundary conditions on the artificial boundaries with just the right properties. There are several classical methods like perfectly matched layers (PML) around the region of interest. They are built to absorb incoming waves (complex stretching of space variable). But unfortunately this does not work for non-homogeneous media. Traditionally, also boundary integral equations were used to construct transparent boundary conditions. But in general, this is not possible for anisotropic media (or heterogenous media, e.g. having periodic properties). The main idea in the work of Sonia Fliss is quite simple: She surrounds the region of interest with half spaces (three or more). Then, the solutions in each of these half spaces are determined by Fourier transform (or Floquet waves for periodic media, respectively). The difficulty is that in the overlap of the different half spaces the representations of the solutions have to coincide. Sonia Fliss proposes a method which ensures that this is true (eventually under certain compatibility conditions). The chosen number of half spaces does not change the method very much. The idea is charmingly simple, but the proof that these solutions exist and have the right properties is more involved. She is still working on making the proofs more easy to understand and apply. It is a fun fact, that complex media were the starting point for the idea, and only afterwards it became clear that it also works perfectly well for homogeneous (i.e. much less complex) media. One might consider this to be very theoretical result, but they lead to numerical simulations which match our expectations and are quite impressive and impossible without knowing the right transparent boundary conditions. Sonia Fliss is still very fascinated by the many open theoretical questions. At the moment she is working at Ecole Nationale Supérieure des Techniques avancées (ENSTA) near Paris as Maitre de conférence. Literature and additional material C. Besse, J. Coatleven, S. Fliss, I. Lacroix-Violet, K. Ramdani: Transparent boundary conditions for locally perturbed infinite hexagonal periodic media, arXiv preprint arXiv:1205.5345, 2012. S. Fliss, P. Joly: Exact boundary conditions for time-harmonic wave propagation in locally perturbed periodic media, Applied Numerical Mathematics 59.9: 2155-2178, 2009. L. Bourgeois, S. Fliss: On the identification of defects in a periodic waveguide from far field data, Inverse Problems 30.9: 095004, 2014.
To separate one single instrument from the acoustic sound of a whole orchestra- just by knowing its exact position- gives a good idea of the concept of wave splitting, the research topic of Marie Kray. Interestingly, an approach for solving this problem was found by the investigation of side-effects of absorbing boundary conditions (ABC) for time-dependent wave problems- the perfectly matched layers are an important example for ABCs. Marie Kray works in the Numerical Analysis group of Prof. Grote in Mathematical Department of the University of Basel. She did her PhD 2012 in the Laboratoire Jacques-Louis Lions in Paris and got her professional education in Strasbourg and Orsay. Since boundaries occur at the surface of volumes, the boundary manifold has one spatial dimension less than the actual regarded physical domain. Therefore, the treatment of normal derivatives as in the Neumann boundary condition needs special care. The implicit Crank-Nicolson method turned out to be a good numerical scheme for integrating the time derivative, and an upwinding scheme solved the discretized hyperbolic problem for the space dimension. An alternative approach to separate the signals from several point sources or scatterers is to apply global integral boundary conditions and to assume a time-harmonic representation. The presented methods have important applications in medical imaging: A wide range of methods work well for single scatterers, but Tumors often tend to spread to several places. This serverely impedes inverse problem reconstruction methods such as the TRAC method, but the separation of waves enhances the use of these methods on problems with several scatterers. Literature and additional material F. Assous, M. Kray, F. Nataf, E. Turkel: Time-reversed absorbing condition: application to inverse problems, Inverse Problems, 27(6), 065003, 2011. F. Assous, M. Kray, F. Nataf: Time reversal techniques for multitarget identification, in Ultrasonics Symposium (IUS), IEEE International (pp. 143-145). IEEE, 2013. M. Grote, M. Kray, F. Nataf, F. Assous: Wave splitting for time-dependent scattered field separation, Comptes Rendus Mathematique, 353(6), 523-527, 2015.
To separate one single instrument from the acoustic sound of a whole orchestra- just by knowing its exact position- gives a good idea of the concept of wave splitting, the research topic of Marie Kray. Interestingly, an approach for solving this problem was found by the investigation of side-effects of absorbing boundary conditions (ABC) for time-dependent wave problems- the perfectly matched layers are an important example for ABCs. Marie Kray works in the Numerical Analysis group of Prof. Grote in Mathematical Department of the University of Basel. She did her PhD 2012 in the Laboratoire Jacques-Louis Lions in Paris and got her professional education in Strasbourg and Orsay. Since boundaries occur at the surface of volumes, the boundary manifold has one spatial dimension less than the actual regarded physical domain. Therefore, the treatment of normal derivatives as in the Neumann boundary condition needs special care. The implicit Crank-Nicolson method turned out to be a good numerical scheme for integrating the time derivative, and an upwinding scheme solved the discretized hyperbolic problem for the space dimension. An alternative approach to separate the signals from several point sources or scatterers is to apply global integral boundary conditions and to assume a time-harmonic representation. The presented methods have important applications in medical imaging: A wide range of methods work well for single scatterers, but Tumors often tend to spread to several places. This serverely impedes inverse problem reconstruction methods such as the TRAC method, but the separation of waves enhances the use of these methods on problems with several scatterers. Literature and additional material F. Assous, M. Kray, F. Nataf, E. Turkel: Time-reversed absorbing condition: application to inverse problems, Inverse Problems, 27(6), 065003, 2011. F. Assous, M. Kray, F. Nataf: Time reversal techniques for multitarget identification, in Ultrasonics Symposium (IUS), IEEE International (pp. 143-145). IEEE, 2013. M. Grote, M. Kray, F. Nataf, F. Assous: Wave splitting for time-dependent scattered field separation, Comptes Rendus Mathematique, 353(6), 523-527, 2015.
Prof. Enrique Zuazua is a Distinguished Professor of Ikerbasque (Basque Foundation for Science) and Founding Scientific Director at the Basque Center for Applied Mathematics (BCAM), which he pushed into life in 2008. He is also Professor in leave of Applied Mathematics at the Universidad Autónoma de Madrid (UAM) and a Humboldt Awardee at the University of Erlangen-Nuremberg (FAU) as well. He was invited by the PDE-group of our Faculty in Karlsruhe to join our work on Wave Phenomena for some days in May 2015. In our conversation he admits that waves have been holding his interest since his work as a PhD student in Paris at the Université Pierre-et-Marie-Curie in the world famous group of Jacques-Louis Lions. Indeed, waves are everywhere. They are visible in everything which vibrates and are an integral part of life itself. In our work as mathematician very often the task is to influence waves and vibrating structures like houses or antennae such that they remain stable. This leads to control problems like feedback control for elastic materials. In these problems it is unavoidable to always have a look at the whole process. It starts with modelling the problem into equations, analysing these equations (existence, uniqueness and regularity of solutions and well-posedness of the problem), finding the right numerical schemes and validating the results against the process which has been modelled. Very often there is a large gap between the control in the discrete process and the numerical approximation of the model equations and some of these differences are explainable in the framework of the theory for hyperbolic partial differential equations and not down to numerical or calculation errors. In the study of Prof. Zuazua the interaction between the numerical grid and the propagation of waves of different frequencies leads to very intuitive results which also provide clear guidelines what to do about the so-called spurious wave phenomena produced by high frequencies, an example of which is shown in this podcast episode image. This is an inherent property of that sort of equations which are able to model the many variants of waves which exist. They are rich but also difficult to handle. This difficulty is visible in the number of results on existence, uniqueness and regularity which is tiny compared to elliptic and parabolic equations but also in the difficulty to find the right numerical schemes for them. On the other hand they have the big advantage that they are best suited for finding effective methods in massively parallel computers. Also there is a strong connection to so-called Inverse Problems on the theoretical side and through applications where the measurement of waves is used to find oil and water in the ground, e.g (see, e.g. our Podcast Modell004 on Oil Exploration). Prof. Zuazua has a lot of experience in working together with engineers. His first joint project was shape optimization for airfoils. The geometric form and the waves around it interact in a lot of ways and on different levels. Also water management has a lot of interesting and open questions on which he is working with colleagues in Zaragoza. At the moment there is a strong collaboration with the group of Prof. Leugering in Erlangen which is invested in a Transregio research initiative on gasnets which is a fascinating topic ranging from our everyday expectations to have a reliable water and gas supply at home to the latest mathematical research on control. Of course, in working with engineers there is always a certain delay (in both directions) since the culture and the results and questions have to be translated and formulated in a relevant form between engineers and mathematicians. In dealing with theses questions there are two main risks: Firstly, one finds wrong results which are obviously wrong and secondly wrong results which look right but are wrong nonetheless. Here it is the crucial role of mathematicians to have the right framework to find these errors. Prof. Zuazua is a proud Basque. Of the 2.5 Mill. members of the basque people most are living in Spain with a minority status of their culture and language. But since the end of the Franco era this has been translated into special efforts to push culture and education in the region. In less than 40 years this transformed the society immensely and led to modern universities, relevant science and culture which grew out of "nothing". Now Spain and the Basque country have strong bonds to the part of Europe on the other side of the Pyrenees and especially with industry and research in Germany. The Basque university has several campuses and teaches 40.000 students. This success could be a good example how to extend our education system and provide possibilities for young people which is so much a part of our culture in Europe across the boundaries of our continent.
Prof. Enrique Zuazua is a Distinguished Professor of Ikerbasque (Basque Foundation for Science) and Founding Scientific Director at the Basque Center for Applied Mathematics (BCAM), which he pushed into life in 2008. He is also Professor in leave of Applied Mathematics at the Universidad Autónoma de Madrid (UAM) and a Humboldt Awardee at the University of Erlangen-Nuremberg (FAU) as well. He was invited by the PDE-group of our Faculty in Karlsruhe to join our work on Wave Phenomena for some days in May 2015. In our conversation he admits that waves have been holding his interest since his work as a PhD student in Paris at the Université Pierre-et-Marie-Curie in the world famous group of Jacques-Louis Lions. Indeed, waves are everywhere. They are visible in everything which vibrates and are an integral part of life itself. In our work as mathematician very often the task is to influence waves and vibrating structures like houses or antennae such that they remain stable. This leads to control problems like feedback control for elastic materials. In these problems it is unavoidable to always have a look at the whole process. It starts with modelling the problem into equations, analysing these equations (existence, uniqueness and regularity of solutions and well-posedness of the problem), finding the right numerical schemes and validating the results against the process which has been modelled. Very often there is a large gap between the control in the discrete process and the numerical approximation of the model equations and some of these differences are explainable in the framework of the theory for hyperbolic partial differential equations and not down to numerical or calculation errors. In the study of Prof. Zuazua the interaction between the numerical grid and the propagation of waves of different frequencies leads to very intuitive results which also provide clear guidelines what to do about the so-called spurious wave phenomena produced by high frequencies, an example of which is shown in this podcast episode image. This is an inherent property of that sort of equations which are able to model the many variants of waves which exist. They are rich but also difficult to handle. This difficulty is visible in the number of results on existence, uniqueness and regularity which is tiny compared to elliptic and parabolic equations but also in the difficulty to find the right numerical schemes for them. On the other hand they have the big advantage that they are best suited for finding effective methods in massively parallel computers. Also there is a strong connection to so-called Inverse Problems on the theoretical side and through applications where the measurement of waves is used to find oil and water in the ground, e.g (see, e.g. our Podcast Modell004 on Oil Exploration). Prof. Zuazua has a lot of experience in working together with engineers. His first joint project was shape optimization for airfoils. The geometric form and the waves around it interact in a lot of ways and on different levels. Also water management has a lot of interesting and open questions on which he is working with colleagues in Zaragoza. At the moment there is a strong collaboration with the group of Prof. Leugering in Erlangen which is invested in a Transregio research initiative on gasnets which is a fascinating topic ranging from our everyday expectations to have a reliable water and gas supply at home to the latest mathematical research on control. Of course, in working with engineers there is always a certain delay (in both directions) since the culture and the results and questions have to be translated and formulated in a relevant form between engineers and mathematicians. In dealing with theses questions there are two main risks: Firstly, one finds wrong results which are obviously wrong and secondly wrong results which look right but are wrong nonetheless. Here it is the crucial role of mathematicians to have the right framework to find these errors. Prof. Zuazua is a proud Basque. Of the 2.5 Mill. members of the basque people most are living in Spain with a minority status of their culture and language. But since the end of the Franco era this has been translated into special efforts to push culture and education in the region. In less than 40 years this transformed the society immensely and led to modern universities, relevant science and culture which grew out of "nothing". Now Spain and the Basque country have strong bonds to the part of Europe on the other side of the Pyrenees and especially with industry and research in Germany. The Basque university has several campuses and teaches 40.000 students. This success could be a good example how to extend our education system and provide possibilities for young people which is so much a part of our culture in Europe across the boundaries of our continent.
Fakultät für Geowissenschaften - Digitale Hochschulschriften der LMU
The scientific investigation of the solid Earth's complex processes, including their interactions with the oceans and the atmosphere, is an interdisciplinary field in which seismology has one key role. Major contributions of modern seismology are (1) the development of high-resolution tomographic images of the Earth's structure and (2) the investigation of earthquake source processes. In both disciplines the challenge lies in solving a seismic inverse problem, i.e. in obtaining information about physical parameters that are not directly observable. Seismic inverse studies usually aim to find realistic models through the minimization of the misfit between observed and theoretically computed (synthetic) ground motions. In general, this approach depends on the numerical simulation of seismic waves propagating in a specified Earth model (forward problem) and the acquisition of illuminating data. While the former is routinely solved using spectral-element methods, many seismic inverse problems still suffer from the lack of information typically leading to ill-posed inverse problems with multiple solutions and trade-offs between the model parameters. Non-linearity in forward modeling and the non-convexity of misfit functions aggravate the inversion for structure and source. This situation requires an efficient exploitation of the available data. However, a careful analysis of whether individual models can be considered a reasonable approximation of the true solution (deterministic approach) or if single models should be replaced with statistical distributions of model parameters (probabilistic or Bayesian approach) is inevitable. Deterministic inversion attempts to find the model that provides the best explanation of the data, typically using iterative optimization techniques. To prevent the inversion process from being trapped in a meaningless local minimum an accurate initial low frequency model is indispensable. Regularization, e.g. in terms of smoothing or damping, is necessary to avoid artifacts from the mapping of high frequency information. However, regularization increases parameter trade-offs and is subjective to some degree, which means that resolution estimates tend to be biased. Probabilistic (or Bayesian) inversions overcome the drawbacks of the deterministic approach by using a global model search that provides unbiased measures of resolution and trade-offs. Critical aspects are computational costs, the appropriate incorporation of prior knowledge and the difficulties in interpreting and processing the results. This work studies both the deterministic and the probabilistic approach. Recent observations of rotational ground motions, that complement translational ground motion measurements from conventional seismometers, motivated the research. It is investigated if alternative seismic observables, including rotations and dynamic strain, have the potential to reduce non-uniqueness and parameter trade-offs in seismic inverse problems. In the framework of deterministic full waveform inversion a novel approach to seismic tomography is applied for the first time to (synthetic) collocated measurements of translations, rotations and strain. The concept is based on the definition of new observables combining translation and rotation, and translation and strain measurements, respectively. Studying the corresponding sensitivity kernels assesses the capability of the new observables to constrain various aspects of a three-dimensional Earth structure. These observables are generally sensitive only to small-scale near-receiver structures. It follows, for example, that knowledge of deeper Earth structure are not required in tomographic inversions for local structure based on the new observables. Also in the context of deterministic full waveform inversion a new method for the design of seismic observables with focused sensitivity to a target model parameter class, e.g. density structure, is developed. This is achieved through the optimal linear combination of fundamental observables that can be any scalar measurement extracted from seismic recordings. A series of examples illustrate that the resulting optimal observables are able to minimize inter-parameter trade-offs that result from regularization in ill-posed multi-parameter inverse problems. The inclusion of alternative and the design of optimal observables in seismic tomography also affect more general objectives in geoscience. The investigation of the history and the dynamics of tectonic plate motion benefits, for example, from the detailed knowledge of small-scale heterogeneities in the crust and the upper mantle. Optimal observables focusing on density help to independently constrain the Earth's temperature and composition and provide information on convective flow. Moreover, the presented work analyzes for the first time if the inclusion of rotational ground motion measurements enables a more detailed description of earthquake source processes. The complexities of earthquake rupture suggest a probabilistic (or Bayesian) inversion approach. The results of the synthetic study indicate that the incorporation of rotational ground motion recordings can significantly reduce the non-uniqueness in finite source inversions, provided that measurement uncertainties are similar to or below the uncertainties of translational velocity recordings. If this condition is met, the joint processing of rotational and translational ground motion provides more detailed information about earthquake dynamics, including rheological fault properties and friction law parameters. Both are critical e.g. for the reliable assessment of seismic hazards.
Mathematical, Statistical and Computational Aspects of the New Science of Metagenomics
Pricop-Jeckstadt, M (University of Bonn) Wednesday 26 March 2014, 14:30-15:00
Oksanen, L (University College London) Wednesday 12 February 2014, 09:45-10:30
Calvetti, D (Case Western Reserve University) Tuesday 11 February 2014, 09:45-10:30
Haber, E (University of British Columbia) Wednesday 12 February 2014, 11:45-12:30
Valkonen, T (University of Cambridge) Friday 07 February 2014, 14:30-15:00
Dorn, O (University of Manchester) Thursday 15 December 2011, 17:00-17:30
Sacks, P (Iowa State University) Thursday 15 December 2011, 09:30-10:00
ten Kroode, F; Smit, D Wednesday 14 December 2011, 16:15-17:00
Schotland, J (University of Michigan) Monday 12 December 2011, 14:30-15:00
Arridge, S (University College London) Monday 12 December 2011, 13:30-14:30
Kachalov, A Tuesday 29 November 2011, 14:00-15:00
Rondi, L (Università degli Studi di Trieste) Friday 26 August 2011, 11:00-11:45
Lassas, M (University of Helsinki) Thursday 25 August 2011, 09:45-10:30
Arridge, S (University College London) Wednesday 24 August 2011, 09:00-09:45
Bal, G (Columbia University) Wednesday 24 August 2011, 09:45-10:30
Kirsch, A (KIT) Thursday 28 July 2011, 09:00-09:45
Kirsch, A (KIT) Thursday 28 July 2011, 11:00-11:45
Kurylev, Y (UCL) Thursday 28 July 2011, 14:00-15:00
Burger, M (Münster) Friday 29 July 2011, 14:00-15:00
Kirsch, A (KIT) Wednesday 27 July 2011, 09:00-09:45
Kirsch, A (KIT) Wednesday 27 July 2011, 11:00-11:45
Avdonin, S (Alaska) Monday 26 July 2010, 16:00-16.45
Avdonin, S (Alaska, Fairbanks) Wednesday 04 April 2007, 17:10-17:40 Quantum Graphs, their Spectra and Applications
We study a group of related problems: the extent to which presence of regular variation of the tail of certain $sigma$-finite measures at the output of a linear filter determines the corresponding regular variation of a measure at the input to the filter. This turns out to be related to presence of a particular cancellation property in $sigma$-finite measures, which, in turn, is related to uniqueness of solutions of certain functional equations. The techniques we develop are applied to weighted sums of iid random variables, to products of independent random variables, and to stochastic integrals with respect to Lévy motions. Joint work with Martin Jacobsen, Thomas Mikosch and Jan Rosinski. Gennady SAMORODNITSKY. Cornell University. Document associé : support de présentation : http://epi.univ-paris1.fr/servlet/com.univ.collaboratif.utils.LectureFichiergw?CODE_FICHIER=1207750230504 (pdf) Ecouter l'intervention : Bande son disponible au format mp3 Durée : 47 mn
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