Scientific Machine Learning(SciML) and It’s Applications

Friday, May 31, 2024

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Course Credit: 0.15 CEU, 1.5 PDH

Friday, May 31, 2024 | 10:00AM – 11:00AM CT

Dr. George Karniadakis is currently Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering at Brown University. He is a renowned authority in the groundbreaking field of Scientific Machine Learning (SciML) and has made significant contributions to SciML, particularly in the areas of Physics-Informed Neural Networks (PINNs) and DeepONets. During his talk, he will delve into these innovative methods and showcase their diverse applications across various domains. Some of the examples include, but not limited to, extracting data from a video to identify systems, using satellite imaging to map temperature and flow fields in the Gulf of Mexico, using Neural operators to build digital twins etc. The webinar at the end will also include a presentation of a GenAI solver that can generate expert-level codes on the fly for any problem given to it.

All content contained within this webinar is copyrighted by George Em Karniadakis and Adar Kahana and its use and/or reproduction outside the portal requires express permission from George Em Karniadakis and Adar Kahana.

Webinar recordings will be available on-demand within 1 business day of the webinar completion.

For those who attended the live webinar, your certificate will be available in your “Learner Profile” within 1 business day of the webinar completion.

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Course Chapters

1Scientific Machine Learning(SciML) and It's Applications
Media Type: Webinar
Dr. George Karniadakis is currently Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering at Brown University. He is a renowned authority in the groundbreaking field of Scientific Machine Learning (SciML) and has made significant contributions to SciML, particularly in the areas of Physics-Informed Neural Networks (PINNs) and DeepONets. During his talk, he will delve into these innovative methods and showcase their diverse applications across various domains. Some of the examples include, but not limited to, extracting data from a video to identify systems, using satellite imaging to map temperature and flow fields in the Gulf of Mexico, using Neural operators to build digital twins etc. The webinar at the end will also include a presentation of a GenAI solver that can generate expert-level codes on the fly for any problem given to it. All content contained within this webinar is copyrighted by George Em Karniadakis and Adar Kahana and its use and/or reproduction outside the portal requires express permission from George Em Karniadakis and Adar Kahana. Webinar recordings will be available on-demand within 1 business day of the webinar completion. For those who attended the live webinar, your certificate will be available in your “Learner Profile” within 1 business day of the webinar completion.

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Adar KahanaSpeakerAdar Kahana is a postdoctoral researcher of Applied Mathematics and Engineering at Brown University, and senior applied scientist at Microsoft. He started his academic affairs at the age of 15, doing a B.Sc. in applied mathematics, minoring in computer science and physics. He served in the Israeli Navy as an electrical engineering researcher and completed his M.Sc. in applied mathematics during that period. He received his Ph.D. from Tel Aviv university in 2021, working for Microsoft and building his industry career at the same time. He started publishing academic papers around 2019 and has already published about 20 papers and 3 approved patent applications. He was also invited, as a leading expert in the field, to peer-review over 10 papers in leading journals. His academic research interests include innovative ways to solve physical, biological, chemical, and related problems using machine learning. In the industrial hat, he currently leads multiple developments of bleeding edge artificial intelligence models, tailored to industries, that unlock the potential of machine learning for the partners and customers of Microsoft.

George Em KarniadakisSpeakerGeorge Karniadakis received his S.M. (1984) and Ph.D. (1987) from Massachusetts Institute of Technology. He was appointed Lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech (1993) in the Aeronautics Department. He joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics on January 1, 1994. He became a full professor on July 1, 1996. He has been a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT since September 1, 2000. He was Visiting Professor at Peking University (Fall 2007 & 2013). He has a joing appointment with PNNL since 2013. He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the Ralf E Kleinman award from SIAM (2015), the (inaugural) J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 102 and he has been cited about 50,500 times (see his google scholar citations). You can also check out his Geneaology Tree (remember to zoom in!). Here is his wikepedia profile. Karniadakis is currently the lead PI of an OSD/ARO/MURI on Fractional PDEs, and previously the lead PI of an OSD/AFOSR MURI on Uncertainty Quantification. He is currently the Director of the DOE center PhILMS on Physics-Informed Learning Machines and previously he was also the Director of the DOE Center of Mathematics for Mesoscale Modeling of Materials (CM4).

Mustafa KaraModeratorMustafa Kara is a Principal Data Scientist at Chevron's Strategic Business Solutions and part of an internal data science consulting team responsible for delivery and support of analytic capabilities to support offshore production facilities in the Gulf of Mexico. As a Principal Data Scientist at Chevron, Mustafa Kara plays a key role in the adoption of digital services in the company’s Gulf of Mexico operations. Prior to that he worked as a Senior Data Scientist for Anadarko Petroleum, having earlier worked as a subsea engineer at DNV GL.