Podcasts about scientific computing

AI is fast becoming the essential technology in the electricity sector for cybersecurity, weather prediction, wildfire vulnerability assessment, grid risk reduction and other things. Host Llewellyn King and Co-host Adam Clayton Powell III discuss AI's potential in the sector with Ron Schoff, Director of R&D at the Electric Power Research Institute (EPRI); and Chris Ritter, Division Director of Scientific Computing & AI at the Idaho National Laboratory (INL).

In this episode of AI, Government, and the Future, host Marc Leh is joined by Manish Parashar, Director of the Scientific Computing and Imaging Institute at the University of Utah. Together, they explore the transformative potential of AI in scientific research, the importance of responsible AI practices, and the role of government in democratizing access to AI technologies. Tune in for an insightful discussion on how AI can address societal challenges while fostering innovation and collaboration across sectors!

Send us a textDisclaimer: The information provided in this podcast is for educational purposes only and is not intended as medical advice. Always consult with a qualified healthcare professional or your medical practitioner before making any changes to your diet, supplementation, or treatment plan, especially if you have a concussion or mild traumatic brain injury (mTBI). The content shared here should not replace personalized care from your doctor or nutritionist.Welcome back to Headfirst: A Concussion Podcast. We're excited to bring you another insightful episode, and today, we have the privilege of speaking with Dr. Federica Conti. Dr. Conti's academic journey is truly impressive—she earned her master's in Mathematical Modelling and Scientific Computing from the prestigious Oxford University, before going on to complete a PhD in Cognitive Neuroscience. Her work stands at the fascinating crossroads of neuroscience, sleep science, and nutrition, with a special focus on how these areas intersect to optimize brain health and protect against neurodegenerative diseases. Dr. Conti's research has made significant contributions to the field, particularly in understanding how lifestyle factors such as sleep patterns and dietary choices can influence brain function. Her ground-breaking papers, including Mitigating Traumatic Brain Injury: A Narrative Review of Supplementation and Dietary Protocols, offer critical insights into how nutrition and supplementation can play a role and possibly reduce the effects of traumatic brain injuries.  -       About the publication (02:25)-       What is a Concussion? (03:59)-       Differences between micronutrients, biological compounds (06:46)Nutrients, Food and Supplementation-       Creatine Monohydrate (08:51)-       Omega-3 Fatty Acids DHA and EPA (17:31) -       Magnesium (27:13)-       BCAA's (32:27)-       Riboflavin and B Vitamins (36:30)-       Top 3 Supplements we should consider mTBI and Cognitive Health (41:00) -       Dr Conti's research, how can people help or find her work? (47:50) Mitigating Traumatic Brain Injury: A Narrative Review of Supplementation and Dietary Protocols: https://www.researchgate.net/publication/382609510_Mitigating_Traumatic_Brain_Injury_A_Narrative_Review_of_Supplementation_and_Dietary_Protocols?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSJ9fQ Dr Conti's Journal Articles: https://www.researchgate.net/profile/Federica-Conti-4 Instagram: @fede.rica.conti

In this episode of the Neil Ashton podcast, Professor Michael Mahoney discusses the intersection of machine learning, mathematics, and computer science. The conversation covers topics such as randomized linear algebra, foundational models for science, and the debate between physics-informed and data-driven approaches. Prof. Mahoney shares insights on the relevance of his research, the potential of using randomness in algorithms, and the evolving landscape of machine learning in scientific disciplines. He also discusses the evolution and practical applications of randomized linear algebra in machine learning, emphasizing the importance of randomness and data availability. He explores the tension between traditional scientific methods and modern machine learning approaches, highlighting the need for collaboration across disciplines. Prof Mahoney also addresses the challenges of data licensing and the commercial viability of machine learning solutions, offering insights for aspiring researchers in the field.Prof. Mahoney website: https://www.stat.berkeley.edu/~mmahoney/Google scholar: https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=enYoutube version: https://youtu.be/lk4lvKQsqWUChapters00:00 Introduction to the Podcast and Guest05:51 Understanding Randomized Linear Algebra19:09 Foundational Models for Science32:29 Physics-Informed vs Data-Driven Approaches38:36 The Practical Application of Randomized Linear Algebra39:32 Creative Destruction in Linear Algebra and Machine Learning40:32 The Role of Randomness in Scientific Machine Learning41:56 Identifying Commonalities Across Scientific Domains42:52 The Horizontal vs. Vertical Application of Machine Learning44:19 The Challenge of Common Architectures in Science46:31 Data Availability and Licensing Issues50:04 The Future of Foundation Models in Science54:21 The Commercial Viability of Machine Learning Solutions58:05 Emerging Opportunities in Scientific Machine Learning01:00:24 Navigating Academia and Industry in Machine Learning01:11:15 Advice for Aspiring Scientific Machine Learning ResearchersKeywordsmachine learning, randomized linear algebra, foundational models, physics-informed neural networks, data-driven science, computational efficiency, academic advice, numerical methods, AI in science, engineering, Randomized Linear Algebra, Machine Learning, Scientific Computing, Data Availability, Foundation Models, Academia, Industry, Research, Algorithms, Innovation

We're experimenting and would love to hear from you!In this episode of Discover Daily, we explore OpenAI's announcement of their latest AI model, o3. This revolutionary model features enhanced reasoning capabilities, improved coding abilities, and innovative chain-of-thought processing. OpenAI's cautious approach includes initial restricted access to safety researchers, with a phased release strategy planning to introduce o3-mini by January 2025.The FDA's comprehensive update to its 'healthy' nutrient content claim marks a significant shift in food labeling standards. The new criteria emphasize nutrient-dense foods while limiting added sugars, allowing previously excluded foods like salmon and avocados to qualify for the 'healthy' label. These changes are projected to save $686 million in chronic disease-related costs over 20 years, though manufacturers face substantial reformulation expenses.The spotlight story features Genesis, dubbed the world's fastest physics engine, developed through collaboration among 20+ research labs. This platform can run simulations up to 430,000 times faster than real-time on an RTX 4090 GPU, with practical applications ranging from robotics to video game development. As an open-source Python platform, Genesis promises to revolutionize physics simulations across multiple industries, from materials science to aerospace engineering.From Perplexity's Discover Feed:https://www.perplexity.ai/page/openai-debuts-o3-model-GxpSIB3_SiGhYuMk_KtczAhttps://www.perplexity.ai/page/fda-s-new-healthy-food-label-_K__20YMRU65jGsxFzHOcAhttps://www.perplexity.ai/page/genesis-world-s-fastest-physic-2_AOTm8gQZ2edfo0ywBX4APerplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

Today's guest is Diego Oliveira Sanchez, founder of NutriAdmin — a nutrition software designed to help nutritionists, health coaches, and personal trainers manage their clients and work more efficiently. Diego discusses how his personal health journey led him to create the software, NutriAdmin's features for consultations, meal planning, client portals, follow-up care, etc., and how the software makes it easy for health coaches to customize nutrition for their clients.   In this episode, we talk about: What is NutriAdmin and how it helps health coaches How poor diet and weight gain in university led Diego to launch NutriAdmin Some common pain points nutritionists and health coaches face Key aspects of the NutriAdmin user experience — the meal plan generator, note-taking during consultations, client portals, etc. NutriAdmin's suitability for other professionals like nurses or mental health specialists How long does it take users to learn how to use NutriAdmin Examples of success clients have seen with NutriAdmin The free trial, money-back guarantee, and resources available on NutriAdmin   Memorable Quotes   “I want to help in any way I can, for more people to have access to nutrition, to great education so that they can improve their health.”   “A lot of people work in the fitness industry, and they are kind of mastering the fitness part. They're coaching their clients, they are doing the exercise, but then the clients are not eating well. And it's probably impossible, or really hard, to get fit if you're exercising but you're not eating well.”   BIO:  Diego is the co-founder of NutriAdmin (nutriadmin.com) - a leading software for nutritionists and coaches launched in 2016. For Diego, NutriAdmin combines his two passions: software and nutrition. Diego has a background in Scientific Computing from Cambridge University and has been writing code since 2009. Diego has also been passionate about nutrition for more than a decade. It's personal. Whilst at uni, Diego gained 35Kg through an unhealthy diet, reaching a dangerous weight. Then, after reading dozens of books in nutrition and with the invaluable help of a nutrition coach, Diego has lost all excess weight, regained his health, and stayed at high fitness levels for over a decade. Diego has seen (on himself and countless others) how nutrition and coaching have tremendous power to transform people's health. The dream with NutriAdmin is to help nutritionists and coaches to reach more clients, so that they regain their health and become their best selves. Links: NutriAdmin Website: https://nutriadmin.com/?via=cathy-sykora (This podcast may include affiliate links. If you sign up through my referral link, I may receive credit or commissions for your purchase. This does not increase or change the cost to you) Email Diego at: diego@nutriadmin.com    Links to resources: Health Coach Group Website https://www.thehealthcoachgroup.com/ Use the code HCC50 to save $50 on our website Leave a Review of the Podcast

NextFlow is a tool for managing scientific computation workflows. It's increasingly popular for bioinformatics, computational biology, and other life science applications. Evan Floden is the Co-Founder and CEO of Seqera Labs which develops NextFlow. He joins the show today to talk about his background as a scientist and engineer, the modular design of NextFlow pipelines, The post Biotech Special: Scientific Computing Pipelines with Evan Floden appeared first on Software Engineering Daily.

NextFlow is a tool for managing scientific computation workflows. It's increasingly popular for bioinformatics, computational biology, and other life science applications. Evan Floden is the Co-Founder and CEO of Seqera Labs which develops NextFlow. He joins the show today to talk about his background as a scientist and engineer, the modular design of NextFlow pipelines, The post Biotech Special: Scientific Computing Pipelines with Evan Floden appeared first on Software Engineering Daily.

Ryan Abernathey is a Climate Scientist, open-source software developer and the CEO & co-founder of Earthmover, a company trying to simplify how scientific computing is done. Ryan also co-founded the Pangeo project in 2016, one of the major efforts to build better tools for scientific computing today.Sponsor: Nimbo by KermapTry out Kermap's monthly mosaic viewer Nimbo for yourselfAbout RyanTwitterLinkedInGithubShownotesNote: Links to books are Amazon Affiliate links. I earn a small commission if you buy any of these books.PangeoXarrayZarrEarthmoverERA5Books & Podcast recommendationCrossing the Chasm by Geoffrey A. Moore (Affiliate Link)The Data Stack ShowTimestamps(00:00) - Introduction(00:45) - Sponsor: Nimbo by Kermap(02:20) - Ryan describes himself(03:11) - From Oceanography to data infrastructure(06:11) - Building an Company around Open Source(13:33) - Product(16:28) - The current Earth Observation data stack(20:39) - Issues with today's approaches(30:30) - Zarr(33:30) - Friction with new technology(38:23) - Climate science vs geospatial(44:48) - Different sciences make different assumptions(47:17) - Modeling Level of Details(59:50) - Book & Podcast recommendations(01:05:37) - Support the podcast on Patreon!Support the podcast on PatreonMy video on an introduction to satellite imagesWebsiteMy TwitterPodcast TwitterRead Previous Issues of the NewsletterEdited by Peter XiongFind more of his work

In this episode Haris Shuaib- Head of Clinical Scientific Computing at Guy's and St Thomas'​ NHS Foundation Trust shares his vision of what is needed to build an AI-ready NHS. Haris shares the story of how he created the first-ever NHS specialist medical AI team and discusses the unique value that individuals with a dual skillset of clinical and technical expertise bring to the NHS, allowing it to become: - an intelligent AI customer - a learning healthcare system - a developer of in-house algorithms to solve problems that wouldn't be tackled by commercial AI companies. Haris also shares the importance of 3 key factors in the success of AI at scale: People, Policy and Platforms

Dr. Renier Mendoza is an Associate Professor of Applied Mathematics at the Institute of Mathematics, University of the Philippines Diliman, where he earned his MS in Applied Mathematics. He holds a doctorate from the Karl-Franzens University of Graz in Austria, and recently completed postdoctoral work at Konkuk University in Seoul, South Korea. His research focuses on numerical optimization, numerical analysis, partial differential equations, delay differential equations, inverse problems, image processing, and mathematical modeling. We talked about negative perceptions about mathematics, mathematics in work and everyday life, challenges in doing mathematical research in the Philippines, applying math in environmental conservation, reasons to pursue a graduate course in mathematics, and more. How to contact Dr. Renier: Facebook: fb.com/renier.mendoza Email: rmendoza@math.upd.edu.ph Interested in DOST scholarship opportunities? Check them out here: https://www.sei.dost.gov.ph/index.php/programs-and-projects/scholarships/postgraduate-scholarships Attention, graduate students and early-career mathematicians from the Philippines and neighboring countries! The 2023 SEAMS School on Scientific Computing for Differential Equations and Applications will take place at the UP Diliman Institute of Mathematics from November 22 to December 1, 2023. It aims to bring together experts in numerical analysis and scientific computing to give introductory lectures on recent numerical techniques in solving ordinary and partial differential equations, and their applications. Funded by the Southeast Asian Mathematical Society through the support of CIMPA, it involves lecture series and hands-on computational exercises. Registration is FREE, but slots are limited. Participants from developing countries in Southeast Asia and nearby regions may also apply for financial support for airfare and/or accommodation. For more information, visit https://math.upd.edu.ph/seamsschoolmanila2023/.

In this episode, we explore Pluto. It is a Julia package, which enables people to ride Pluto notebooks. These notebooks are used by hundreds of thousands of people around the world to help explore the world around them. We talked to Fons to understand how he and some of his co-creators started this project, what motivated them to create yet another notebook framework and what are the key differences towards other notebooks, such as Jupiter, MATLAB Live editor, and maybe a little bit of Observable. In addition to this, we will also explore how Pluto's are used in education with the Gerhard's experience as a teacher and a community manager, as the team collects a wide variety of use cases of where and how Pluto is used and how it helped people explore the world around. But not only do we understand and explore how it got going, how it's used today, we also take a sneak preview of what's to come in the future and understand how the community and the team make their decisions of what are the new features to be implemented and why. And it all revolves around some of their guiding principles that the team has put in place.   Useful links that are referred in the episodehttps://featured.plutojl.orghttps://github.com/fonsp/Pluto.jlhttps://julialang.zulipchat.com/Support the Show.

This episode features a super fun conversation with Ramya Gurunathan, PhD who pursued a MS in Scientific Computing followed by a PhD in Materials Science. Her experiences and journey are super cool and I'm excited for you all to hear about it! Ramya and I also discussed a lot of lessons learned and tips for anyone in a PhD program right now. Hope you enjoy the episode! Follow us on Instagram at @impactfactorpod Logo design by Rebecca Van Aken. Music by Katie Van Aken. --- Support this podcast: https://anchor.fm/katie-van-aken/support

Why the next kernel will be "the merge window from hell," a holiday gift for Wayland users, and how the open source community could do more to take on YouTube.

Why the next kernel will be "the merge window from hell," a holiday gift for Wayland users, and how the open source community could do more to take on YouTube.

This episode's conversation between host Peter Wang and Ryan Abernathey, Associate Professor at Columbia University in the City of New York, explores climate science, scientific computing, data accessibility, and more.    Topics that Peter and Ryan cover include: - Cloud computing - Open data and collaboration - Climate science and the private sector - Open-source projects like Pangeo Forge and Xarray   Climate data is sometimes restricted in the way it flows between interested parties; the growth of private industry around data storage and dissemination has put up barriers to entry that can limit access to valuable systems and data. This is especially troubling to Ryan because these barriers often exclude some of the people who are most affected by climate change. He feels that usable information can and should be made accessible without undermining private interests.   Peter Wang - https://www.linkedin.com/in/pzwang/ Ryan Abernathey - https://www.linkedin.com/in/ryan-abernathey-32a70652/ Columbia University in the City of New York - https://www.linkedin.com/school/columbia-university/ Pangeo Forge - https://pangeo-forge.org/ Xarray - https://docs.xarray.dev/en/stable/   You can find a human-verified transcript of this episode here. - https://know.anaconda.com/rs/387-XNW-688/images/ANACON_%20Ryan%20Abernathey_HVT.docx.pdf   If you enjoyed today's show, please leave a 5-star review. For more information, visit https://www.anaconda.com/podcast.  

Danica Fine, Senior Developer Advocate at Confluent, joins this episode to talk about how Confluent provides tools for creating a streaming data platform. We discuss what Confluent is and the problems it solves, Confluent Cloud, Kafka, some interesting use cases and much more! Danica graduated with a B.A. in Logic, Information, and Computation and a Masters of Science in Engineering in Scientific Computing from the University of Pennsylvania. Danica worked as a software engineer for almost 5 years at Bloomberg, where she worked on streaming infrastructure. Danica has been working at Confluent as a Senior Developer Advocate for a little over one year. Links: Danica's twitter, Confluent's YouTube channel, Confluent's engineering blog You can support this podcast on the anchor page. Make sure to subscribe and follow Alexa's Input Twitter account to get notified when a new podcast episode comes out. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/alexagriffith/support

This week we have scientific computing educator extraordinaire, Harrison Kinsley on the show! Harrison is the founder of multiple businesses, all of which leverage the Python programming language. From using Flask web development on all of his business sites to Scikit Learn and TensorFlow for machine learning and data analysis with Ensmo.com to the Natural Language Toolkit for natural language processing with Sentdex.com to teaching a massive variety of Python programming topics on PythonProgramming.net -- Python and programming is a major part of Harrison's life and work. Harrison likes to learn and build with technology which he teaches on his YouTube channel with over a million subscribers. Harrison believes programming is a superpower, and the social impact of making programming education easily accessible to anyone is one of the most important things he can do with his life. Harrison's Python Website: www.pythonprogramming.net Harrison's Python YouTube: www.youtube.com/c/sentdex Harrison's Twitter: www.twitter.com/sentdex Harrison's Instagram: www.instagram.com/sentdex Who is Puget Systems? Puget Systems is based in the Seattle suburb of Auburn, WA, and specializes in high-performance, custom-built computers. We believe that computers should be a pleasure to purchase and own. They should get your work done, and not be a hindrance. Our goal is to provide each client with the best possible computer for their needs and budget. Learn more about Puget Systems: www.pugetsystems.com Learn more about our Scientific Computing Solutions: www.pugetsystems.com/solutions/scientific/index.php --- Send in a voice message: https://anchor.fm/puget-systems/message

Gregory Kurtzer Gregory is the Founder and Chief Executive Officer at Ctrl IQ, Inc and the Founder of CentOS and Rocky Linux. https://www.linkedin.com/in/gmkurtzer/ https://github.com/gmkurtzer https://gmkurtzer.github.io https://ctrliq.com/ Notes: MPI Hello world - https://mpitutorial.com/tutorials/mpi-hello-world/ HPL Linpack - https://www.netlib.org/benchmark/hpl/ OpenHPC Linux Foundation Project - https://linuxfoundation.org/press-release/high-performance-computing-leaders-unite-to-develop-open-source-framework/ Warewulf - https://github.com/hpcng/warewulf Credits: Music by ikson: https://www.iksonmusic.com Special Guest: Gregory Kurtzer.

Developing Artificial General Intelligence that Learns by Dreaming. View the full video interview here. Brent Oster is the President and CEO of ORBAI. He has 28 years experience in 3D computer graphics, animation, simulation, and AI with Bioware, Electronic Arts, Autodesk, and NVIDIA. He was the co-founder Bioware and Check Six, and he has completed the Stanford Continuing Studies curriculum of classes in entrepreneurial business, along with his degrees in Aerospace Engineering at University of Toronto and Scientific Computing at UC Santa Barbara. As a Sr Solution Architect at NVIDIA, Brent helped Fortune 500 companies (and startups) looking to adopt ‘AI', but consistently found that DL architectures tools fell far short of their expectations for ‘AI'. Brent started ORBAI to develop something better for them

Alfio Quarteroni is Professor of Numerical Analysis and Director of of the Laboratory for Modeling and Scientific Computing -- otherwise known as MOX -- at the Polytechnic University of Milan in Italy. He is the founder (and first director) of MOX and of MATHICSE at EPFL, Lausanne, where he is Emeritus Professor. He is co-founder (and President) of MOXOFF, a spin-off company. His research interests concern Mathematical Modelling, Numerical  Analysis, Scientific Computing, and applications in fluid mechanics, geophysics, medicine, epidemiology, and the improvement of sports performance. His research group at EPFL has contributed to the preliminary design of Solar Impulse, the Swiss, long-range experimental solar-powered aircraft project; they also carried out the mathematical simulation optimising the performances of the Alinghi yacht, twice winner of the America's Cup. He authored or edited 37 books and contributed more than 400 articles to international scientific journals and conference proceedings. He also serves on many editorial boards of journals and book series.He is a plenary speaker at ECM 2021, where he will give a talk on Mathematical Modeling of the Cardiac FunctionRelated Books and Journals and Springer homepage of the podcast: https://www.springer.com/gp/campaign/mathematics-podcasts

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments. This Perspective describes the development and capabilities of SciPy 1.0, an open source scientific computing library for the Python programming language. 2020: Pauli Virtanen, R. Gommers, T. Oliphant, Matt Haberland, Tyler Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, Jonathan Bright, Stéfan J. van der Walt, M. Brett, Joshua Wilson, K. Millman, Nikolay Mayorov, Andrew R. J. Nelson, E. Jones, Robert Kern, Eric Larson, C. J. Carey, Ilhan Polat, Y. Feng, Eric W. Moore, J. Vanderplas, D. Laxalde, Josef Perktold, R. Cimrman, I. Henriksen, E. Quintero, Charles R. Harris, A. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, P. van Mulbregt, Aditya Alessandro Pietro Alex Andreas Andreas Anthony Ant Vijaykumar Bardelli Rothberg Hilboll Kloeckner Sco, A. Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, A. Hilboll, Andre Kloeckner, A. Scopatz, Antony Lee, A. Rokem, C. N. Woods, Chad Fulton, C. Masson, C. Häggström, Clark Fitzgerald, David A. Nicholson, David R. Hagen, D. Pasechnik, E. Olivetti, E. Martin, E. Wieser, Fabrice Silva, F. Lenders, Florian Wilhelm, G. Young, Gavin A. Price, G. Ingold, Gregory E. Allen, Gregory R. Lee, H. Audren, I. Probst, J. Dietrich, J. Silterra, James T. Webber, J. Slavic, J. Nothman, J. Buchner, Johannes Kulick, Johannes L. Schönberger, J. V. de Miranda Cardoso, J. Reimer, J. Harrington, J. Rodríguez, Juan Nunez-Iglesias, Justin Kuczynski, K. Tritz, M. Thoma, M. Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, M. Pak, Nathaniel J. Smith, N. Nowaczyk, Nikolay Shebanov, O. Pavlyk, P. A. Brodtkorb, Perry Lee, R. McGibbon, Roman Feldbauer, Sam Lewis, S. Tygier, Scott Sievert, S. Vigna, Stefan Peterson, S. More, Tadeusz Pudlik, T. Oshima, T. Pingel, T. Robitaille, Thomas Spura, T. Jones, T. Cera, Tim Leslie, Tiziano Zito, Tom Krauss, U. Upadhyay, Y. Halchenko, Y. Vázquez-Baeza Keywords: SciPy, Python, Sparse matrix, Computational science, Algorithm, Linear algebra, Computational geometry, Interpolation, Signal processing, Image processing, Cluster analysis, Programming language, Library (computing), Machine learning, Input/output, Black Hole, Open-source software, File spanning, Computation https://arxiv.org/pdf/1907.10121v1.pdf

Gareth Thomas is the co-founder of  VersionBay (https://www.versionbay.com) and is one of the co-hosts of Tangible Computing. In this episode, you will about his company and his views on the Technical and Scientific computing landscape. We will also drill down into how he got hooked on using MATLAB at University and hear how and where people learn about Technical Computing and Scientific Computing.

Associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute visits the PolicyViz Podcast to talk about the Visualization Design Lab. The post Episode # 199: Miriah Meyer appeared first on PolicyViz.

Associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute visits the PolicyViz Podcast to talk about the Visualization Design Lab. The post Episode # 199: Miriah Meyer appeared first on PolicyViz.

This week, join us for a live Q&A session with Dr. Don Kinghorn, Scientific Computing Advisor at Puget Systems as we discuss Docker containers, enroot, and how the A100 is performing for the scientific community. Every week at 1pm Pacific we host a LIVE Q&A show featuring members of our Labs team and industry experts ranging from machine learning scientists to indie filmmakers, professional photographers, sound designers, VFX artists, and more! --- Send in a voice message: https://anchor.fm/puget-systems/message

Ivan Oseledets is a Professor of the Center for Computational and Data-Intensive Science and Engineering at Skoltech. Ivan graduated from Moscow Institute of Physics and Technology in 2006, got Candidate of Sciences degree in 2007, and Doctor of Sciences in 2012, both from Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences. He joined Skoltech CDISE in 2013. Ivan's research covers a broad range of topics. He proposed a new decomposition of high-dimensional arrays (tensors) – tensor-train decomposition, and developed many efficient algorithms for solving high-dimensional problems. These algorithms are used in different areas of chemistry, biology, data analysis and machine learning. His current research focuses on development of new algorithms in machine learning and artificial intelligence such as construction of adversarial examples, theory of generative adversarial networks and compression of neural networks. It resulted in publications in top computer science conferences such as ICML, NIPS, ICLR, CVPR, RecSys, ACL and ICDM. Ivan is an Associate Editor of SIAM Journal on Mathematics in Data Science, SIAM Journal on Scientific Computing, Advances in Computational Mathematics (Springer). He is also an area chair of ICLR 2020 conference. Ivan got several awards for his research and industrial cooperation, including two gold medals of Russian academy of Sciences (for students in 2005 and young researchers in 2009), Dynasty Foundation award (2012), SIAM Outstanding Paper Prize (2018), Russian President Award for young researchers in science and innovation (2018), Ilya Segalovich award for Best PhD thesis supervisor (2019), Best Professor award from Skoltech (2019), the best cooperation project leader award from Huawei (2015, 2017). He also has been a Pi and Co-Pi of several grants and industrial projects (230 million of rubles since 2017). In 2021, Ivan became one of the winners of the Humboldt Research Award, is an award given by the Alexander von Humboldt Foundation of Germany. Ivan is actively involved in education and research supervision: he introduced and is teaching three courses of Skoltech curriculum, and five of his PhD students have successfully defended their theses, including two PhD students at Skoltech. FIND IVAN ON SOCIAL MEDIA LinkedIn | Facebook | Instagram | Twitter © Copyright 2022 Den of Rich. All rights reserved.

Ivan Oseledets is a Professor of the Center for Computational and Data-Intensive Science and Engineering at Skoltech.Ivan graduated from Moscow Institute of Physics and Technology in 2006, got Candidate of Sciences degree in 2007, and Doctor of Sciences in 2012, both from Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences. He joined Skoltech CDISE in 2013.Ivan's research covers a broad range of topics. He proposed a new decomposition of high-dimensional arrays (tensors) – tensor-train decomposition, and developed many efficient algorithms for solving high-dimensional problems. These algorithms are used in different areas of chemistry, biology, data analysis and machine learning. His current research focuses on development of new algorithms in machine learning and artificial intelligence such as construction of adversarial examples, theory of generative adversarial networks and compression of neural networks. It resulted in publications in top computer science conferences such as ICML, NIPS, ICLR, CVPR, RecSys, ACL and ICDM.Ivan is an Associate Editor of SIAM Journal on Mathematics in Data Science, SIAM Journal on Scientific Computing, Advances in Computational Mathematics (Springer). He is also an area chair of ICLR 2020 conference.Ivan got several awards for his research and industrial cooperation, including two gold medals of Russian academy of Sciences (for students in 2005 and young researchers in 2009), Dynasty Foundation award (2012), SIAM Outstanding Paper Prize (2018), Russian President Award for young researchers in science and innovation (2018), Ilya Segalovich award for Best PhD thesis supervisor (2019), Best Professor award from Skoltech (2019), the best cooperation project leader award from Huawei (2015, 2017). He also has been a Pi and Co-Pi of several grants and industrial projects (230 million of rubles since 2017). In 2021, Ivan became one of the winners of the Humboldt Research Award, is an award given by the Alexander von Humboldt Foundation of Germany.Ivan is actively involved in education and research supervision: he introduced and is teaching three courses of Skoltech curriculum, and five of his PhD students have successfully defended their theses, including two PhD students at Skoltech.FIND IVAN ON SOCIAL MEDIALinkedIn | Facebook | Instagram | Twitter

This week Dr. Don Kinghorn joins us to talk about Intel oneAPI, Rocket Lake, Ice Lake, Nvidia GTC, and more! Dr. Kinghorn is our resident Ph.D. and scientist. He is the escalation point for questions concerning HPC, Scientific Computing, Machine Learning, AI, Linux System Administration, and Troubleshooting. He also works on hardware systems validation for scientific workloads and provides an internal educational resource for others here at Puget Systems. --- Send in a voice message: https://anchor.fm/puget-systems/message

Peter Wang talks about his journey of being the CEO of and co-founding Anaconda, his perspective on the Python programming language, and its use for scientific computing. Peter Wang has been developing commercial scientific computing and visualization software for over 15 years. He has extensive experience in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging. Peter’s interests in the fundamentals of vector computing and interactive visualization led him to co-found Anaconda (formerly Continuum Analytics). Peter leads the open source and community innovation group. As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating and teaching Python at conferences around the world. Peter holds a BA in Physics from Cornell University. Follow peter on Twitter: https://twitter.com/pwang​ https://www.anaconda.com/​ Intake: https://www.anaconda.com/blog/intake-...​ https://pydata.org/​ Scientific Data Management in the Coming Decade paper: https://arxiv.org/pdf/cs/0502008.pdf Topics covered: 0:00​ (intro) Technology is not value neutral; Don't punt on ethics 1:30​ What is Conda? 2:57​ Peter's Story and Anaconda's beginning 6:45​ Do you ever regret choosing Python? 9:39​ On other programming languages 17:13​ Scientific Data Management in the Coming Decade 21:48​ Who are your customers? 26:24​ The ML hierarchy of needs 30:02​ The cybernetic era and Conway's Law 34:31​ R vs python 42:19​ Most underrated: Ethics - Don't Punt 46:50​ biggest bottlenecks: open-source, python Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on these other platforms: YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://wandb.ai/gallery

We take it upon ourselves to test and validate each industry's most popular applications and understand the nuances of how they interact with and utilize PC hardware. And while we do publish extensive articles on these subjects, we also realize that not everyone has the time to understand and apply this knowledge to their specific needs. That's why we have our Labs Open Office Hour! --- Send in a voice message: https://anchor.fm/puget-systems/message

Nick Hinton is known as a conspiracy theory gurus on Twitter; he first got major attention for his Saturn Time Cube posts-----Intro - 00:15Ads - 07:44 - Please visit our sponsors Awakened Vapes, Genesis Pharms, and the Caramel Corn Company and help support this podcast!Interview - 11:19-----Welcome back to Awakened Exchanges! I'm your host Jay Rich and this is our first holiday episode! This whole month I'll be focused on fun things that bring me, and hopefully you, joy. Our first episode this month is with Nick Hinton himself, and if the name sounds familiar, that's probably because you know him as “the conspiracy guy on Twitter.” If everything goes as planned, we should have a very special Christmas Eve with a former member of the Borg themselves! Stay tuned for more info!My introduction to Nick was because of a Mandela Effect post that happened to catch my eye. I've been interested in the subject since my introduction to the Berenstain vs Berenstein Bears phenomenon. Once I started researching, I found that there were a few things that stuck with me on the anomalous side, whereas a few I remembered “the correct way.” While I'm sure that memory is malleable, I also started seriously considering what bigger implications could be made. I'm a believer in Hugh Everett's Multiverse Hypothesis, but I like to think of the decision tree as branchial time,  where each decision can branch off into multiple lines. If most of those decisions only affect you or a limited number of people, then you can think of those branches eventually being clipped back off into what we'll call the current consensus reality. My particular thought is, what if the Mandela Effect was actually caused by larger branches of time that were merged on top of one another? Two similar universes, with minor differences in the timeline, dimensionally merged together somehow. This is where I bring up CERN and the fact that the Large Hadron Collider was built in 2008...the year before Fiona Broome launched the Mandela Effect website to ask people if they also remembered Nelson Mandela's funeral, even though he didn't die until 2013. There was talk early on about the collider possibly causing miniature black holes, but scientists weren't concerned as they would burn themselves out before reaching critical mass. That said, in November 2009, Sergio Bertolucci, the Director for Research and Scientific Computing at CERN, said that what they were attempting to do was akin to opening a door and that, I quote, "Out of this door might come something, or we might send something through it." That may have you thinking about Stranger Things and The Upside Down, but what if that door was just to one, or many, very similar realities that share space with us here and now? More in the podcast.I get the feeling that there are a large number of our audience this week that will be coming here to hear Nick speak.  Nick is known as one of the conspiracy theory gurus on Twitter and he first got major attention for his Saturn Time Cube posts. He had compiled a lot of information from various sources and made it a lot more accessible for psychonauts and conspiracy buffs to make it through, eventually publishing his first book on the subject.  I get the feeling that we could have gotten going on a number of topics and I hope to have him back on the show someday to do just that. This was one of my favorite conversations yet, and I truly hope that you enjoy this fun little holiday gift to kick off December. Support the show (https://www.patreon.com/AwakenedExchanges) --- Send in a voice message: https://podcasters.spotify.com/pod/show/awakened-exchanges/message Support this podcast: https://podcasters.spotify.com/pod/show/awakened-exchanges/support

In this episode, Abe Stern, Sr. Data Scientist on the Healthcare Team with NVIDIA, begins by defining the process of drug discovery, its goals and challenges. Abe then discusses how the process is being modernized, how scientific computing is playing a role in drug discovery and the role of GPUs in this process. Abe concludes by sharing the resources NVIDIA offers to researchers looking to get started, a description of the partnership with Dell, customer examples, where to find more info and final thoughts.

In this episode we will talk about the Python community and the scientific Python ecosystem. So if you always wanted to know what is so great about Python for Machine learning and its community this episode is for you.More Information: Python and TensorflowWhat is DVCRun Jupyter NotebooksCreate a Free account (Azure)Deep Learning vs. Machine Learning Get Started with Machine LearningDon't miss new episodes, subscribe to the AI Show

In this episode, we have the pleasure of hosting Dr. Don Kinghorn! Don is the escalation point for sales and support questions concerning HPC, Scientific Computing, Machine Learning, AI, Linux System Administration, and Hardware Troubleshooting. He works on hardware systems validation for Scientific workloads and provides an internal educational resource for others here at Puget Systems. If you read our publications with any frequency, you’ll see Don contributes often to our HPC Blog. Don has been with Puget Systems since 2013 and has a long history with scientific and high-performance computing that was nurtured by his educational background in Computational/Theoretical Chemistry. --- Send in a voice message: https://anchor.fm/puget-systems/message

A first-generation Guatemalan and Salvadoran immigrant, Jacobo Pereira-Pacheco is now preparing to leave the Bay Area as he enters the world of academia again and begins his PhD studies in Statistics and Scientific Computing at UC Santa Cruz. We had an inspiring conversation in which we discussed the world of Data Science and what a Data Practitioner does, the similarities between Statistics and Environmental Studies, and the queer Latinx community.  Additional Reading Mentioned in the Interview Python: https://www.python.org/ R: https://www.r-project.org/about.html Julia: https://julialang.org/ Udemy: https://www.udemy.com Khan Academy: https://www.khanacademy.org/ FKA twigs: https://fkatwi.gs/   Our website is LatinxInPower.com. Send me a message on Instagram @Latinx_in_Power or Twitter @LatinxInPower. Check out our new episodes every first Tuesday of the month.

Springer has put out a ton of awesome textbooks, and they made over 500 of them available for free download, including a couple dozen tech ebooks!An Introduction to Machine Learning, 2nd ed. 2017 by Miroslav KubatAutomata and Computability, 1997 by Dexter C. KozenComputational Geometry, 3rd ed. 2008 by Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark OvermarsComputer Vision, 2011 by Richard SzeliskiConcise Guide to Databases, 2013 by Peter Lake, Paul CrowtherConcise Guide to Software Engineering, 1st ed. 2017 by Gerard O'ReganCryptography Made Simple, 1st ed. 2016 by Nigel SmartData Mining, 2015 by Charu C. AggarwalData Structures and Algorithms with Python, 2015 by Kent D. Lee, Steve HubbardDigital Image Processing, 2nd ed. 2016 by Wilhelm Burger, Mark J. BurgeEye Tracking Methodology, 3rd ed. 2017 by Andrew T. DuchowskiFoundations for Designing User-Centered Systems, 2014 by Frank E. Ritter, Gordon D. Baxter, Elizabeth F. ChurchillFoundations of Programming Languages, 2nd ed. 2017 by Kent D. LeeFundamentals of Business Process Management, 2013 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersFundamentals of Multimedia, 2nd ed. 2014 by Ze-Nian Li, Mark S. Drew, Jiangchuan LiuGuide to Competitive Programming, 1st ed. 2017 by Antti LaaksonenGuide to Computer Network Security, 4th ed. 2017 by Joseph Migga KizzaGuide to Discrete Mathematics, 1st ed. 2016 by Gerard O'ReganIntroduction to Artificial Intelligence, 2nd ed. 2017 by Wolfgang ErtelIntroduction to Data Science, 1st ed. 2017 by Laura Igual, Santi SeguíIntroduction to Deep Learning, 1st ed. 2018 by Sandro SkansiIntroduction to Evolutionary Computing, 2nd ed. 2015 by A.E. Eiben, J.E. SmithLaTeX in 24 Hours, 1st ed. 2017 by Dilip DattaModelling Computing Systems, 2013 by Faron Moller, Georg StruthObject-Oriented Analysis, Design and Implementation, 2nd ed. 2015 by Brahma Dathan, Sarnath RamnathPrinciples of Data Mining, 3rd ed. 2016 by Max BramerProbability and Statistics for Computer Science, 1st ed. 2018 by David ForsythPython Programming Fundamentals, 2nd ed. 2014 by Kent D. LeeRecommender Systems, 1st ed. 2016 by Charu C. AggarwalThe Algorithm Design Manual, 2nd ed. 2008 by Steven S SkienaThe Data Science Design Manual, 1st ed. 2017 by Steven S. SkienaThe Python Workbook, 2014 by Ben StephensonUML @ Classroom, 2015 by Martina Seidl, Marion Scholz, Christian Huemer, Gerti KappelUnderstanding Cryptography, 2010 by Christof Paar, Jan PelzlFundamentals of Business Process Management, 2nd ed. 2018 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersGuide to Scientific Computing in C++, 2nd ed. 2017 by Joe Pitt-Francis, Jonathan WhiteleyFundamentals of Java Programming, 1st ed. 2018 by Mitsunori OgiharaLogical Foundations of Cyber-Physical Systems, 1st ed. 2018 by André PlatzerNeural Networks and Deep Learning, 1st ed. 2018 by Charu C. AggarwalSystems Programming in Unix/Linux, 1st ed. 2018 by K.C. WangIntroduction to Parallel Computing, 1st ed. 2018 by Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut RobičAnalysis for Computer Scientists, 2nd ed. 2018 by Michael Oberguggenberger, Alexander OstermannIntroductory Computer Forensics, 1st ed. 2018 by Xiaodong Linhttps://www.springernature.com/gp/librarians/the-link/blog/blogposts-ebooks/free-access-to-a-range-of-essential-textbooks/17855960

Springer has put out a ton of awesome textbooks, and they made over 500 of them available for free download, including a couple dozen tech ebooks!An Introduction to Machine Learning, 2nd ed. 2017 by Miroslav KubatAutomata and Computability, 1997 by Dexter C. KozenComputational Geometry, 3rd ed. 2008 by Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark OvermarsComputer Vision, 2011 by Richard SzeliskiConcise Guide to Databases, 2013 by Peter Lake, Paul CrowtherConcise Guide to Software Engineering, 1st ed. 2017 by Gerard O'ReganCryptography Made Simple, 1st ed. 2016 by Nigel SmartData Mining, 2015 by Charu C. AggarwalData Structures and Algorithms with Python, 2015 by Kent D. Lee, Steve HubbardDigital Image Processing, 2nd ed. 2016 by Wilhelm Burger, Mark J. BurgeEye Tracking Methodology, 3rd ed. 2017 by Andrew T. DuchowskiFoundations for Designing User-Centered Systems, 2014 by Frank E. Ritter, Gordon D. Baxter, Elizabeth F. ChurchillFoundations of Programming Languages, 2nd ed. 2017 by Kent D. LeeFundamentals of Business Process Management, 2013 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersFundamentals of Multimedia, 2nd ed. 2014 by Ze-Nian Li, Mark S. Drew, Jiangchuan LiuGuide to Competitive Programming, 1st ed. 2017 by Antti LaaksonenGuide to Computer Network Security, 4th ed. 2017 by Joseph Migga KizzaGuide to Discrete Mathematics, 1st ed. 2016 by Gerard O'ReganIntroduction to Artificial Intelligence, 2nd ed. 2017 by Wolfgang ErtelIntroduction to Data Science, 1st ed. 2017 by Laura Igual, Santi SeguíIntroduction to Deep Learning, 1st ed. 2018 by Sandro SkansiIntroduction to Evolutionary Computing, 2nd ed. 2015 by A.E. Eiben, J.E. SmithLaTeX in 24 Hours, 1st ed. 2017 by Dilip DattaModelling Computing Systems, 2013 by Faron Moller, Georg StruthObject-Oriented Analysis, Design and Implementation, 2nd ed. 2015 by Brahma Dathan, Sarnath RamnathPrinciples of Data Mining, 3rd ed. 2016 by Max BramerProbability and Statistics for Computer Science, 1st ed. 2018 by David ForsythPython Programming Fundamentals, 2nd ed. 2014 by Kent D. LeeRecommender Systems, 1st ed. 2016 by Charu C. AggarwalThe Algorithm Design Manual, 2nd ed. 2008 by Steven S SkienaThe Data Science Design Manual, 1st ed. 2017 by Steven S. SkienaThe Python Workbook, 2014 by Ben StephensonUML @ Classroom, 2015 by Martina Seidl, Marion Scholz, Christian Huemer, Gerti KappelUnderstanding Cryptography, 2010 by Christof Paar, Jan PelzlFundamentals of Business Process Management, 2nd ed. 2018 by Marlon Dumas, Marcello La Rosa, Jan Mendling, Hajo A. ReijersGuide to Scientific Computing in C++, 2nd ed. 2017 by Joe Pitt-Francis, Jonathan WhiteleyFundamentals of Java Programming, 1st ed. 2018 by Mitsunori OgiharaLogical Foundations of Cyber-Physical Systems, 1st ed. 2018 by André PlatzerNeural Networks and Deep Learning, 1st ed. 2018 by Charu C. AggarwalSystems Programming in Unix/Linux, 1st ed. 2018 by K.C. WangIntroduction to Parallel Computing, 1st ed. 2018 by Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut RobičAnalysis for Computer Scientists, 2nd ed. 2018 by Michael Oberguggenberger, Alexander OstermannIntroductory Computer Forensics, 1st ed. 2018 by Xiaodong Linhttps://www.springernature.com/gp/librarians/the-link/blog/blogposts-ebooks/free-access-to-a-range-of-essential-textbooks/17855960

On this episode, we'll speak with one of the principal researchers on the Wolfram Physics Project, Jonathan Gorard. We'll discuss how they plan to actually implement their “fundamental theory of the universe” and build these computational universes. Jonathan Gorard is a theoretical mathematician, researcher at the University of Cambridge, and consultant to Wolfram Research. He is ostensibly the scientist tasked with doing the maths to demonstrate these concepts to the scientific community and the public. Actually proving these ideas out in the open, over the din of more than a few detractors, may be one of the most difficult parts of this highly complex project. Along with a call for scientists everywhere to contribute and critique the project in an open-source manner, the team published nearly a thousand pages and over 500 hours of video. All since Wolfram's announcement on April 14th titled, “We may have found a fundamental theory of the universe… and it's beautiful.” The Wolfram Physics team, consisting of Stephen Wolfram, Jonathan Gorard, and theoretical physicist and competitive programmer Max Piskanov, are literally live-streaming the entire thing. Scientific discovery, open collaboration, and peer review happening in real-time, and anyone can get involved. Now that is beautiful. This is the TomorrowScale podcast, hosted by Justin Briggs. Wolfram Physics Project Website: https://www.wolframphysics.org/ Announcement by Stephen Wolfram: https://writings.stephenwolfram.com/2020/04/finally-we-may-have-a-path-to-the-fundamental-theory-of-physics-and-its-beautiful/ Registry of Notable Universes: https://www.wolframphysics.org/universes/ Jonathan Gorard, Cambridge University Center for Scientific Computing: https://www.csc.cam.ac.uk/academic/MPhilSciComp/directory/gorard http://TomorrowScale.com The TomorrowScale Podcast was created to showcase scientists and entrepreneurs who are building science-based businesses, and to hear their stories from the benches and in the trenches of research & development. The views expressed by the host and guests are their own, and the content of this show should not be considered legal, tax, or investing advice. Thanks to our guests for sharing their time and knowledge with us. Thank you for listening. Please science responsibly. --- Support this podcast: https://anchor.fm/tomorrowscale/support

In episode 27, we interviewed Ralf Gommers from the NumPy and SciPy projects. We started our discussion by talking about his past research experience as a physicist and his transition to open source software and programming. This led him to get involved in projects such as PyWavelets, NumPy and SciPy. Following that, we had a great discussion about NumPy, its many features, its target audience and its performance. We learned why NumPy is not included in Python's standard library and its overlap with Scipy. We also compared the combination of Matlab to NumPy and Python and how users could transition to this open source solution. We then had a brief discussion about SciPy and the features it provides. Ralf informed us of the positive results from Google's previous Summer of Code and Season of Docs participations. We discussed how to reach the project and the many kinds of contributions that they are looking for. We talked about the importance of FLOSS for science and attribution of research output. We finished the interview with our classic quick questions and a reflection from Ralf about the need for more sustainability in open source software development as volunteer effort may not be sufficient in the future. 00:00:00 Intro 00:00:18 Introduction 00:00:33 Introducing Ralf Gommers 00:02:05 Research during his PhD and and PostDoc 00:03:20 When he started to use open source tools 00:03:52 Learning to code 00:04:39 PyWavelets, another sideproject he likes 00:05:55 His elevator pitch for NumPy 00:06:55 Vector arrays in Python before NumPy 00:07:49 How he got involved in the NumPy project 00:10:13 Traget users for NumPy 00:11:36 NumPy as part of the standard library? 00:13:24 Features provided by NumPy 00:14:22 Major differences between Python built-in list and NumPy's array 00:16:01 Structured data 00:16:45 Why appending a row to an array is made hard 00:18:09 Multithreaded code with NumPy 00:19:48 Distributed array processing 00:20:50 GPU computation with Python and NumPy 00:22:16 Linear algebra functions in NumPy 00:23:25 Overlap between SciPy and NumPy for linear algebra 00:23:55 Python speed as an interpreted language 00:25:43 Python with NumPy compared to Matlab 00:28:07 How easy is the transition between Matlab and Python Numpy 00:29:26 Performance difference between Matlab and Python 00:31:00 Commercial applications of NumPy 00:32:15 Contributions from the industry ans incentives to contribute 00:34:10 Elevator pitch for SciPy 00:35:37 Overview of some of the submodules in SciPy 00:38:11 The size of the communities 00:39:33 Participation in Google Summer of Code 00:40:24 Participation in Google Season of Docs 00:41:48 Communication channels in the project 00:43:25 Where to ask for support? 00:44:48 Possible contributions 00:46:25 Skills usefull to contribute to the NumPy project 00:48:12 Identifying possible contributions 00:48:52 The importance of FLOSS for science 00:52:02 Possible negative impact of FLOSS on science 00:52:49 Crediting contributions in science 00:53:42 Most notable scientific discovery in recent years 00:54:49 His favourite text processing tool 00:55:30 Volunteer effort may not be sufficient anymore 00:56:58 Contact informations for Ralf Gommers 00:57:27 Outro

Did you come into Python from a computational science side of things? Were you just looking for something better than Excel or Matlab and got pulled in by all the Python has to offer? That's great! But following that path often means some of the more formal practices from software development weren't part of the journey. On this episode, you'll meet Martin Héroux, who does data science in the context of academic research. He's here to share his best practices and lessons for data scientists of all sorts. Links from the show Neuroscience Research Australia: neura.edu.au Martin Héroux: researchgate.net Errors in science: I make them do you? Part 3: scientificallysound.org PyPI Packages DABEST: pypi.org/project/dabest PSYCHOPY: pypi.org/project/PsychoPy Spreadsheet Blunders 12 of the Biggest Spreadsheet Fails: blogs.oracle.com Common spreadsheet errors: datacarpentry.org Best Practices for Scientific Computing: journals.plos.org Good enough practices in scientific computing: journals.plos.org Full episode RSS feed: talkpython.fm/episodes/rss_full_history Springboard bootcamp scholarships [code TALKPYTHONTOME]: talkpython.fm/springboard Sponsors Clubhouse Linode Talk Python Training

Episode Notes: CODAR, an Exascale Computing Project co-design center, aims to produce an infrastructure for online data analysis and reduction.

This week on our debut episode I discuss Hyperspace with Dr Gaurav Khanna www.linkedin.com/in/gaurav-khanna a Professor in the Physics Department, and the Associate Director of the Center for Scientific Computing at the University of Massachusetts Dartmouth. He works on a variety of challenging problems in theoretical and computational physics. Dr Gaurav introduces me to hyperspace and blackholes and as a non-scientist (although learning everyday) I learn very quickly that in theory travel through a blackhole won't necessarily be dangerous at all. In fact science indicates the 'Yes' if you have a large enough blackhole and if it is spinning fast enough then travel through would be safe and that the main issue is what is on the other side of the singularity? Further Reading and Resources:Life of the Cosmos's written by Lee Smolin: https://www.bookdepository.com/Life-Cosmos-Lee-Smolin/9780195126648?ref=grid-view&qid=1574913237375&sr=1-6&a_aid=widdershins&a_bid=ba35a05bRotating black holes may serve as gentle portals for hyperspace travel: http://theconversation.com/rotating-black-holes-may-serve-as-gentle-portals-for-hyperspace-travel-107062This episode was inspired by the book: The Hyperspace Trap - Christopher G Nuttall https://widdershinspodcast.com/season-one%3A-space-1 See more from Dr Gaurav Khanna at: http://gravity.phy.umassd.edu Follow Widdershins and please Rate and Review us in your favourite Podcast app so others can easily find Widdershins!Visit our website: www.widdershinspodcast.com/ for member only access and merchandiseFacebook: www.facebook.com/WiddershinsPodcast/Twitter: https://twitter.com/WiddershinsPInstagram: https://www.instagram.com/widdershins_podcast/Email us: connect@widdershinspodcast.com Widdershins proudly uses the services of Letitia Stafford – The Ultimate Podcasting Virtual Assistant

Thank you for listening to the Leaders, Innovators and Big Ideas podcast, supported by Rainforest Alberta.  The podcast that highlights those people who are contributing to and/or supporting the innovation ecosystem in Alberta. This episode is hosted by Allan Marston.  Allan is a Father, Husband and innovator. Mr. Marston is a successful corporate executive heading up departments in Business Development, Sales, Marketing and Human Resources. As a serial entrepreneur Allan has over 18 years’ experience in the technology startup space and was the CEO of his own Silicon Valley company.  Previously Allan spent 25 years in the retail business and was with a company when it created Canada's first loyalty reward card program.  He has had several start-ups and successful exits and is currently the founder of Zenoshi.io where he is building a universal rewards program on the blockchain.  Neeraj Gupta is an active angel investor, mentor and serial entrepreneur in Deeptech domain. Neeraj is an expert on Entrepreneurship, Angel Investment, Startup Ecosystem Building, Patent Consulting and Innovation Management. He advises several provincial and federal governments on cross-border trade, innovation and entrepreneurship.He is Founder and Managing Director at Lawcubator, Founder & CEO at FormulateIP, Cofounder at Chapter.AI, co-founder of PanIIT Mentors,Neeraj holds Bachelors of Technology degree from Indian Institute of Technology (IIT) in Metallurgical and Material Sciences and Masters of Science Degree from Royal Institute of Technology (KTH), Sweden in Scientific Computing and Mathematics. Neeraj is PGPX in General Management from UCLA Anderson School of Management.Neeraj is author of the book "Creating & safeguarding a strong Intellectual Property Portfolio" which was written in collaboration with SIDBI, Adelphi and KFW Bank Germany.Quote(s):"Canada is in the top 10 for both entrepreneurship and innovation."Summary:Discussion on patents and saving money using provisional patents. Also some insight into when and how startups should engage with angel investors.  Please be sure to share this episode with everyone you know. If you are interested in being either a host, a guest, or a sponsor of the show, please reach out.  We are published in Google Podcasts and the iTunes store for Apple Podcasts.  We would be grateful if you could give us a rating as it helps spread the word about the show. Credits... This Episode Sponsored By: Workhaus Core Graphic Design: Mackenzie Bedford Episode Music: Tony Del Degan Creator & Producer: Al Del Degan 

In episode 21, we interviewed Juan Pablo Carbajal, an Argentinian physicist currently working as a postdoctoral researcher in the Department of Urban Water Management at the ETH domain in Switzerland. We had a great discussion about GNU Octave and how it can help scientists. We compared its core functions and its expandability through packages to its commercial equivalent Matlab and its toolboxes. An interesting feature of GNU Octave that we explored with Juan is the possibility to migrate code from Matlab directly to GNU Octave and to a certain point maintain code compatible with both. Juan shared with us that since the introduction of an integrated GUI in 2015, he noticed a continuous growth in popularity for the project. We then discussed about a few of the reasons why companies are interested by GNU Octave and why universities should teach using free/libre software. Before asking our usual quick questions, Juan talked with us about the reasons why FLOSS is important for science and the importance of exposing non-FLOSS users to the benefits of FLOSS. 00:00:00 Message to our listeners 00:00:29 Intro 00:00:45 Introducing Juan Pablo Carbajal 00:01:32 30 seconds elevator pitch for GNU Octave 00:02:20 How does the Octave programming language compares to other common programming languages 00:03:23 Compatibility between GNU Octave and Matlab 00:06:29 Matlab's toolboxes compared to GNU Octave packages 00:07:31 Simulink models with GNU Octave 00:09:06 Parallel processing with GNU Octave 00:10:40 The issue with CUDA in GNU Octave 00:11:48 How GNU Octaves differs rom other open source Matlab equivalents 00:13:34 Syntax compatibility to ease transition and reusing code from Matlab 00:15:11 Resources to start using GNU Octave 00:16:40 GNU Octave's graphical user interface and the old QT Octave GUI 00:20:14 GNU Octave's graphical user interface compared to Matlab 00:22:11 Why GNU Octave and not simply Octave 00:23:06 GNU Octave licence 00:24:01 How often he uses GNU Octave 00:24:18 Juan's numerous contributions to the project 00:25:27 GNU octave for companies 00:27:45 Arguments for teaching with GNU Octave instead of Matlab 00:29:32 How many are involved in the project? 00:30:37 Communication channels within the project 00:31:34 Is the project actively looking for developers? 00:32:11 Skills required to contribute 00:33:14 The two-level language dilemma 00:34:59 Juan's vision about FLOSS and its importance for science 00:37:09 Possible negative impacts of FLOSS and converting non-FLOSS users 00:40:17 The most notable scientific discovery in recent years 00:41:46 Juan's favourite text processing tools 00:42:38 Things we forgot to ask about 00:43:57 Anything else to share? 00:44:25 How to contact Juan 00:44:50 Outro

Hugo speaks with Andrew Gelman about statistics, data science, polling, and election forecasting. Andy is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University and this week we’ll be talking the ins and outs of general polling and election forecasting, the biggest challenges in gauging public opinion, the ever-present challenge of getting representative samples in order to model the world and the types of corrections statisticians can and do perform. "Chatting with Andy was an absolute delight and I cannot wait to share it with you!"-Hugo Links from the show FROM THE INTERVIEWAndrew's Blog Andrew on Twitter We Need to Move Beyond Election-Focused Polling (Gelman and Rothschild, Slate)We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results (Cohn, The New York Times).19 things we learned from the 2016 election (Gelman and Azari, Science, 2017)The best books on How Americans Vote (Gelman, Five Books)The best books on Statistics (Gelman, Five Books)Andrew's Research FROM THE SEGMENTSStatistical Lesson of the Week (with Emily Robinson at ~13:30)The five Cs (Loukides, Mason, and Patil, O'Reilly)Data Science Best Practices (with Ben Skrainka~40:40)Oberkampf & Roy’s Verification and Validation in Scientific Computing provides a thorough yet very readable treatment A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (Roy and Oberkampf, Science Direct) Original music and sounds by The Sticks.

Our guest today is Dr. Mario Lassnig, a software engineer working on the ATLAS Experiment at CERN! Melanie and Mark put on their physics hats as they learn all about what it takes to manage the petabytes of data involved in such a large research project. Dr. Mario Lassnig Dr. Mario Lassnig has been working as a Software Engineer at the European Organisation for Nuclear Research (CERN) since 2006. Within the ATLAS Experiment, he is responsible for all aspects of its large-scale distributed data, including management, storage, network, and access. He is also one of the principal developers of the Rucio system for scientific data management. In his previous life, he developed mobile navigation software for multi-modal transportation in Vienna at Seibersdorf Research, as well as cryptographic smart-card applications for access control at the University of Klagenfurt. He holds a Master’s degree in Computer Science from the University of Klagenfurt, and a doctoral degree in Computer Science from the University of Innsbruck. Cool things of the week The Machines Can Do the Work, a Story of Kubernetes Testing, CI, and Automating the Contributor Experience blog Google Cloud grants $9M in credits for the operation of the Kubernetes project blog Improving job searches for veterans with Google Cloud’s Talent Solution blog Unity For Beginners… From a Beginner blog GCP Podcast Episode 134: Connected Games with Unity and Google Cloud with Brett Bibby and Micah Baker podcast Neural Information Processing Systems Conference site Interview Rucio - Scientific Data Management site CERN site ATLAS site Google Cloud Storage site Google Compute Engine site G Suite site GKE On-Prem site Rucio on GitHub site University of Oslo site University of Innsbruck site Brookhaven National Laboratory site University of Texas at Arlington site Square Kilometer Array site DUNE site LIGO Lab site Scientific Computing with Google Cloud Platform: Experiences from the Trenches in Particle Physics and Earth Sciences video GCP Podcast Episode 122: Project Jupyter with Jessica Forde, Yuvi Panda and Chris Holdgraf podcast Rucio Workshop site ACM/IEEE Supercomputing 2018 site Question of the week I am not familiar with Docker or Kubernetes - where can I get started? Docker Docker’s official “Getting Started” guide Katacoda’s free, interactive Docker course Kubernetes You should totally read this comic and interactive tutorial Katacoda’s free, interactive Kubernetes course Where can you find us next? Melanie will be at Deep Learning Indaba. Mark will be at Tokyo NEXT. We’ll both be at Strange Loop.

Researcher Dr. Eva Lee joins Tim to discuss her work on the front lines in the battle against the opioid epidemic. Dr. Lee is a professor in the School of Industrial and Systems Engineering at Georgia Tech, and Director of the Center for Operations Research in Medicine and HealthCare, and her not-so-secret weapons are math, data and analytics. https://traffic.libsyn.com/shapingopinion/Opioids_-_Start_by_protecting_the_innocents_-_Episode_27.mp3   In this episode, we talk with Dr. Lee about her work in trying to tackle challenging problems associated with the nation’s opioid epidemic and how perceptions in the medical community is one key area of focus. This bonus episode is a break from our normal pattern at the Shaping Opinion podcast. Usually, we talk about people, events or things that have found a place in history that truly have shaped the way we think. The nation’s opioid epidemic is different, however. The opioid epidemic is happening now. It’s not history. Our perceptions of the seeming harmlessness of a painkiller or a cough medicine may lead us to choose comfort over temporary discomfort, which has the potential to lead to complications from taking opioids. When we talked to Dr. Eva Lee, we learned that math can be used to identify patient care practices with the best outcomes, and that if those practices spread, society can start to take significant measures to counter the opioid epidemic. Dr. Lee focused her research on the youngest and most vulnerable among us, babies. But not healthy babies. These are babies born with heart defects that typically begin their lives in the Intensive Care Unit and face serious surgeries in the first year of their lives. They are prescribed opioids to alleviate their suffering. But where do we get to the point where the opioids can cause more problems than they solve? And most importantly, what can we learn from Dr. Lee’s research in this area to expand those lessons to children and adults so that the nation can form a more broad-based attack on the opioid epidemic? Dr. Lee is a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, and she is Director of the Center for Operations Research in Medicine and HealthCare. She is also a Senior Research Professor at the Atlanta VA Medical Center. She uses mathematical programming and large-scale computational algorithms to help medical and healthcare decision-making. She tackles challenging problems in health systems and biomedicine by bringing a math perspective to healthcare, through systems modeling, algorithm and software design, and decision theory analysis. This work creates a better understanding of what works based on data and analytics so that patient care guidelines across the country can be improved. Dr. Lee was part of a team that was a finalist for a prestigious honor for this work. It is the Franz Edelman Award from the Institute for Operations Research and the Management Sciences (INFORMS). We talked to Dr. Eva Lee about her research and what it means from actual health care practices, to doctor and patient perceptions. Links Dr. Lee Bio (Georgia Tech) The Most Interesting Person in the O.R. World, INFORMS Pediatric Heart Network, Edelman Award Finalist, INFORMS About this Episode's Guest Dr. Eva Lee Eva K Lee is a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, and Director of the Center for Operations Research in Medicine and HealthCare. She is also a Senior Research Professor at the Atlanta VA Medical Center. Dr. Lee earned a Ph.D. at Rice University in the Department of Computational and Applied Mathematics, and received her undergraduate degree in Mathematics from Hong Kong Baptist University, where she graduated with Highest Distinction. Dr. Lee was awarded a NSF/NATO postdoctoral fellowship on Scientific Computing,

Das Treffens des German Chapters of European Women in Mathematics fand am 3. und 4. Mai 2018 im Mathematikon in Heidelberg statt. Am Rande der Konferenz der Mathematikerinnen sprach Gudrun mit Susanne Krömker. Sie leitet seit 2004 die Computergrafik-Gruppe des Interdisziplinären Zentrums für Wissenschaftliches Rechnen(IWR) an der Ruprecht-Karls-Universität Heidelberg. In der Computergrafik geht es grob gesagt um Analyse und Bearbeitung von Daten und ihre Darstellung als visuelle Information, d.h. es kommen sehr viele unterschiedliche Anforderungen zusammen. Andererseits sind die Themen, die jeweils dahinter stecken auch ganz besonders vielgestaltig. Für Susanne begann 1989 die Faszination mit der Darstellung einer reaktiven Strömung, bei der explosionsartig Wärme freigesetzt wird. Im Experiment ist die Apparatur geborsten, die Simulation liefert die Erklärung durch eine von den Wänden reflektierte Druckwelle und die Visualisierung macht den zeitlich enorm kurzen Explosionsvorgang mit Temperatur- und Druckverteilung im reaktiven Gemisch anschaulich. Anschließend hat sie sich in ihrer Promotion mit partiellen Differentialgleichungen zur Beschreibung katalytischer Prozesse an Oberflächen beschäftigt, sich aber nie ganz von dem Thema Computergrafik getrennt, das in den 1990er Jahren dann richtig Fahrt aufnahm. Heute ist die Computergrafik technisch gesehen eine typische Anwendung für Hochleistungsrechnen. Außerdem gibt es immer wieder interessante Entwicklungen, die die Möglichkeiten von Grafikkarten unkonventionell ausnutzen. Aber es geht auch darum, geeignete Methoden zu entwicklen und zu implementieren, mit denen die von 3D-Scannern erfassten Messdaten auf ihren Informationsgehalt reduziert werden können. Grundsätzlich müssen dabei immer dreidimensionale Vorgänge auf dem zweidimensionalen Bildschirm dargestellt werden. Dazu braucht man projektive Geometrie - ein Thema, das in der Vorlesung mitunter abstrakt und von der Realität weit entfernt scheint. In ihrer Geometrie-Vorlesung für Sudierende der Mathematik kann Susanne ihre Erfahrungen aus der Informatik sehr anschaulich einbringen wie hier im Video für die Fano Ebene: (YouTube) Etwa seit dem Jahr 2000 gab es in der Arbeitsgruppe von Susanne viele besonders interessante und sehr unterschiedliche Projekte. Ein Forschungsschwerpunkt lag dabei in der Kombination von Computertomographie und Oberflächendaten, um aus beiden Bildgebungsverfahren im Resultat ein verbessertes Bild zu erhalten: ILATO-Projekt (Improving Limited Angle computed Tomography by Optical data integration). Außerdem hat sich die enge Zusammenarbeit mit Archäologen, Historikern und Geologen als besonders fruchtbar erwiesen. Beispiele dafür sind der theoriebildende Diskurs zum digitalen Modell des Klosters Lorsch (seit 1991 Weltkulturerbe der UNESCO) oder die Rekonstruktion von Inschriften in Tafeln und auf Grabsteinen, z.B. auf dem jüdischen Friedhof in Worms. Diese Analyse basiert auf Multiskalen Integralinvarianten, einem Filterverfahren, das in dem Softwareframework GigaMesh (Autor: Hubert Mara, IWR, Universität Heidelberg) implementiert ist. Oder die Rekonstruktion der Karten von barocken Globen, u.a. des Gottorfer Globus, mit HiIfe von anisotrop diffusiver Filterung. Die Arbeitsgruppe hat Ausgrabungen im Tempelgebiet von Angkor in Kambodscha, dem größten Tempelgebiet der Welt, wissenschaftlich begleitet. Es gelang eine virtuelle architektonische Rekonstruktion des größten Tempels Angkor Wat oder die Rekonstruktion einer sechs Meter hohen Schiva-Statue aus Koh Ker, von der einzelne Puzzle-Teile über mehrere Museen der Welt verstreut sind. Susanne hatte sich 1983 zunächst für ein Studium der Mathematik und Betriebswirtschaft in Osnabrück entschieden, hat dann aber den Wechsel nach Heidelberg verbunden mit der Hinwendungen zu anderen Natur- und Geisteswissenschaften nie bereut. Literatur und weiterführende Informationen Inschrift aus Gisela-Grab entziffert, Historisches Museum der Pfalz Speyer / Universität Heidelberg, 7. November 2016 © damals.de S. Krömker: Digitales Modell Kloster Lorsch - Ein Ladenburger Diskurs zum Thema virtueller Rekonstruktion in: Sonderheft Sehenswerte, Schlösser und Gärten Hessen, 1/2015. A. Beyer, H. Mara and S. Krömker: ILATO Project: Fusion of Optical Surface Models and Volumetric CT Data in: arXiv:1404.6583, 2014. S. Krömker: Neue Methoden zur besseren Lesbarkeit mittelalterlicher Grabsteine am Beispiel des Heiligen Sands in Worms, in: Die SchUM-Gemeinden Speyer - Worms - Mainz. Auf dem Weg zum Welterbe. Schnell & Steiner, pp. 167 - 188, 2013. H. G. Bock, W. Jäger, M. J. Winckler (eds.), Scientific Computing and Cultural Heritage, Contributions in Mathematical and Computational Sciences, Volume 3, Springer, 2013. A. Hoffmann, F. Zimmermann, H. Scharr, S. Krömker, C. Schulz: Instantaneous three-dimensional visualization of concentration distributions in turbulent flows with crossed-plane laser-induced fluorescence imaging, Applied Physics B: Lasers and Optics, 2004. Fosdick, L.D., E.R. Jessup, C.J.C. Schauble, and G. Domik, An Introduction to High-Performance Scientific Computing, MIT Press, 0-262-06181-3, 750 pp, 1995.

Gudrun hatte zwei Podcast-Gespräche beim FEniCS18 Workshop in Oxford (21.-23. März 2018). FEniCS ist eine Open-Source-Plattform zur Lösung partieller Differentialgleichungen mit Finite-Elemente-Methoden. Dies ist die zweite der beiden 2018er Folgen aus Oxford. Susanne Claus ist zur Zeit NRN Early Career Personal Research Fellow an der Cardiff University in Wales. Sie hat sich schon immer für Mathematik, Physik, Informatik und Ingenieursthemen interesseirt und diese Interessen in einem Studium der Technomathematik in Kaiserlautern verbunden. Mit dem Vordiplom in der Tasche entschied sie sich für einen einjährigen Aufenthalt an der Universität Kyoto. Sie war dort ein Research exchange student und hat neben der Teilnahme an Vorlesungen vor allem eine Forschungsarbeit zu Verdunstungsprozessen geschrieben. Damit waren die Weichen in Richtung Strömungsrechnung gestellt. Dieses Interesse vertiefte sie im Hauptstudium (bis zum Diplom) an der Uni in Bonn, wo sie auch als studentische Hilfskraft in der Numerik mitarbeitete. Die dabei erwachte Begeisterung für nicht-Newtonsche Fluid-Modelle führte sie schließlich für die Promotion nach Cardiff. Dort werden schon in langer Tradition sogenannte viskoelastische Stoffe untersucht - das ist eine spezielle Klasse von nicht-Newtonschem Fluiden. Nach der Promotion arbeitet sie einige Zeit als Postdoc in London am University College London (kurz: UCL) zu Fehleranalyse für Finite Elemente Verfahren (*). Bis sie mit einer selbst eingeworbenen Fellowship in der Tasche wieder nach Cardiff zurückkehren konnte. Im Moment beschäftigt sich Susanne vor allem mit Zweiphasenströmungen. In realen Strömungsprozessen liegen eigentlich immer mindestens zwei Phasen vor: z.B. Luft und Wasser. Das ist der Fall wenn wir den Wasserhahn aufdrehen oder die Strömung eines Flusses beobachten. Sehr häufig werden solche Prozesse vereinfacht modelliert, indem man sich nur eine Phase, nämlich die des Wassers genau ansieht und die andere als nicht so wichtig weglässt. In der Modellbildung für Probleme, in denen beide Phasen betrachtet werden sollen, ist das erste Problem, dass das physikalische Verhalten der beiden Phasen sehr unterschiedlich ist, d.h. man braucht in der Regel zwei sehr unterschiedliche Modelle. Hinzu treten dann noch komplexe Vorgänge auf der Grenzflächen auf z.B. in der Wechselwirkung der Phasen. Wo die Grenzfläche zu jedem Zeitpunkt verläuft, ist selbst Teil der Lösung des Problems. Noch interessanter aber auch besonders schwierig wird es, wenn auf der Grenzfläche Tenside wirken (engl. surfactant) - das sind Chemikalien die auch die Geometrie der Grenzfläche verändern, weil sie Einfluß auf die Oberflächenspannung nehmen. Ein Zwischenschritt ist es, wenn man nur eine Phase betrachtet, aber im Fließprozess eine freie Oberfläche erlaubt. Die Entwicklung dieser Oberfläche über die Zeit wird oft über die Minimierung von Oberflächenspannung modelliert und hängt deshalb u.a. mit der Krümmung der Fläche zusammen. D.h. man braucht im Modell lokale Informationen über zweite Ableitungen. In der numerischen Bearbeitung des Prozesses benutzt Susanne das FEniCS Framework. Das hat sie auch konkret dieses Jahr nach Oxford zum Workshop geführt. Ihr Ansatz ist es, das Rechengitter um genug Knoten anzureichern, so dass Sprünge dargestellt werden können ohne eine zu hohe Auflösung insgesamt zu verursachen. (*) an der UCL arbeitet auch Helen Wilson zu viscoelastischen Strömungen, mit der Gudrun 2016 in Oxford gesprochen hat. Literatur und weiterführende Informationen S. Claus & P. Kerfriden: A stable and optimally convergent LaTIn-Cut Finite Element Method for multiple unilateral contact problems, CoRR, 2017. H. Oertel jr.(Ed.): Prandtl’s Essentials of Fluid Mechanics, Springer-Verlag, ISBN 978-0-387-21803-8, 2004. S. Gross, A. Reusken: Numerical Methods for Two-phase Incompressible Flows, Springer-Verlag, eBook: ISBN 978-3-642-19686-7, DOI 10.1007/978-3-642-19686-7, 2011. E. Burman, S. Claus & A. Massing: A stabilized cut finite element method for the three field Stokes problem. SIAM Journal on Scientific Computing 37.4: A1705-A1726, 2015. Podcasts G. Thäter, R. Hill: Singular Pertubation, Gespräch im Modellansatz Podcast, Folge 162, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. H. Wilson: Viscoelastic Fluids, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 92, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2016.