Neural operator: A new paradigm for learning PDEs by Animashree Anandkumar Published 2021-04-12 Download video MP4 360p Recommendations 51:33 DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar 1:05:33 Fourier Neural Operator for Parametric Partial Differential Equations (Paper Explained) 1:18:53 George Karniadakis - From PINNs to DeepOnets 12:13 The Most Infamous Graduate Physics Book 58:12 DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators. 1:32:01 Diffusion and Score-Based Generative Models 1:06:35 MedAI #41: Efficiently Modeling Long Sequences with Structured State Spaces | Albert Gu 55:02 Zongyi Li's talk on solving PDEs from data 51:22 Rethinking Physics Informed Neural Networks [NeurIPS'21] 1:10:36 Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | The Science Circle 26:56 Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning 17:50 How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile 55:15 MIT 6.S191: Convolutional Neural Networks 1:12:47 Andrew Stuart - Supervised Learning For Operators 35:33 Neural Ordinary Differential Equations 16:56 Stable Diffusion in Code (AI Image Generation) - Computerphile 59:56 Anima Anandkumar - Neural operator: A new paradigm for learning PDEs 49:22 Introduction to PINNs Similar videos 32:45 A crash course on Neural Operators 29:33 Neural Networks for Solving PDEs 41:34 PDENA22: Physics-informed Neural Networks: A new paradigm for learning physical laws 55:30 Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar 59:40 Anima Anandkumar - AI4Science: A Revolution in the Making (April 21, 2021) 27:47 Constructing Informative Features for Discriminative Learning 01:17 Accelerating Carbon Capture and Storage With Fourier Neural Operator and NVIDIA Modulus 1:04:55 IAIFI Colloquium: AI Accelerating Sciences: Neural operators for Learning Between Function Spaces 38:47 "Introduction to physics-informed neural networks" Liu Yang (Brown) - CFPU SMLI 1:32:53 ETH Zürich DLSC: Physics-Informed Neural Networks - Applications 22:18 Dr. Anima Anandkumar (NVIDIA) | AI for Science | TransformX 2022 More results