DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar скачать в хорошем качестве

DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar

We will present exciting developments in the use of AI for scientific applications. This includes diverse domains such as weather and climate modeling, deep earth modeling, etc. We have developed principled approaches that enables zero-shot generalization beyond the training domain. This includes neural operators that yield 4-5 orders of magnitude speedups over numerical weather models and other scientific simulations. They learn mappings between function spaces that makes them ideal for capturing multi-scale processes. Bio: Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She has received several honors such as Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She is passionate about designing principled AI algorithms and applying them in interdisciplinary applications. Her research focus is on unsupervised AI, optimization, and tensor methods. DDPS webinar: https://www.librom.net/ddps.html 💻 LLNL News: https://www.llnl.gov/news 📲 Instagram:   / livermore_lab   🤳 Facebook:   / livermore.lab   🐤 Twitter:   / livermore_lab   🔔 Subscribe: / livermorelab About LLNL: Lawrence Livermore National Laboratory has a mission of strengthening the United States’ security through development and application of world-class science and technology to: 1) enhance the nation’s defense, 2) reduce the global threat from terrorism and weapons of mass destruction, and 3) respond with vision, quality, integrity and technical excellence to scientific issues of national importance. Learn more about LLNL: https://www.llnl.gov/. LLNL-VIDEO-848789