Chris Rackauckas - Generalizing Scientific Machine Learning and Differentiable Simulation Published 2023-11-24 Download video MP4 360p Recommendations 44:17 Cristian Axenie - Physics-informed Machine Learning for Robust Pedestrian Detection 1:28:45 Nathan Kutz - The Dynamic Mode Decomposition - A Data-Driven Algorithm 1:37:37 The Turing Lectures: The future of generative AI 31:36 The Special Math of Translating Theory to Software in Differential Eqs | Chris Rackauckas | ASE60 28:38 [CFD] Turbulence Intensity for RANS 21:57 What is Lie theory? Here is the big picture. | Lie groups, algebras, brackets #3 2:49:31 Automatic Differentiation and SciML | Chris Rackauckas | JuliaHEP 2023 14:04 Battery Energy Revolution. What now? 3:50:57 How Deep Neural Networks Work - Full Course for Beginners 58:12 MIT Introduction to Deep Learning | 6.S191 3:54:38 Optogenetics: Illuminating the Path toward Causal Neuroscience 1:15:08 Demystifying the Higgs Boson with Leonard Susskind 06:30 Data Analytics vs Data Science 1:25:04 Data Science Job Interview – Full Mock Interview 06:28 Principal Component Analysis (PCA) 43:15 Keynote: Scientific Machine Learning Through Symbolic Numerics | Chris Rackauckas | JuliaCon 2023 57:21 An observation on Generalization 3:55:13 Wolfram Physics Project: Working Session Wednesday, May 6, 2020 [Finding Black Hole Structures] 3:54:12 Wolfram Physics Project: Relations to Category Theory 56:48 The Impact of chatGPT talks (2023) - Keynote address by Prof. Yann LeCun (NYU/Meta) Similar videos 57:39 DDPS|Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models 1:02:29 Chris Rackauckas: Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation 39:26 Introduction to Scientific Machine Learning in Astroinformatics Part 1: Applications 54:15 Generalized Physics-Informed Learning through Language-Wide Differentiable Programming by Rackauckas 1:13:36 Improving Model Discovery with Imposed Structures in Scientific Machine Learning | ML4Science 25:50 Extending Scientific Machine Learning (SciML) to Agent-Based Models (ICLR AI4ABM 2023) 34:08 Chris Rackauckas Integrating solvers w/ probabilistic programming through differentiable programming 1:08:00 Universal Differential Equations for SciML - Modeling and Computation Seminar, Chris Rackauckas 1:20:05 Differentiable Simulation and Scientific Machine Learning: Fast Solving,Automated Model Construction 25:32 State of SciML Scientific Machine Learning | Chris Rackauckas | SciMLCon 2022 1:02:53 Scientific Machine Learning and Stiffness - MIT Institute for AI and Fundamental Interactions IAIFI 47:25 Stiffness in Scientific Machine Learning: Cornell SCAN Seminar 1:36:11 Baking Knowledge into Machine Learning Models—Chris Rackauckas on TechLifeSkills w/ Tanmay Ep.55 47:58 Differentiable Programming Part 1: Reverse-Mode AD Implementation 29:12 Learned Models for Physical Simulation and Design | AI & Engineering | Kimberly Stachenfeld More results