Scientific Machine Learning seminars
70 videos • 1,482 views • by Amirhossein Arzani
1
Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
Steve Brunton
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2
Maximilian Herde: Poseidon: Efficient Foundation Models for PDEs (Tutorial 1)
MICDE University of Michigan
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3
Differentiable Programming for Data-driven Modeling, Optimization, and Control
Fields Institute
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4
KAN: Kolmogorov-Arnold Networks | Ziming Liu
Valence Labs
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5
Domain decomposition||Physics-based methods in computational cardiology|| Seminar on March 22, 2024
CRUNCH Group: Home of Math + Machine Learning + X
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6
DDPS | ‘Physics Informed Machine Learning through Symbolic Regression’
Inside Livermore Lab
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7
Michael Shields: UQ for ML and ML for UQ
MICDE University of Michigan
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8
Modeling Coupled 1D PDEs of Cardiovascular Flow with Spatial Neural ODEs, NeurIPS ML4PS 2023
Csala Hunor
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9
Nathan Kutz - The Dynamic Mode Decomposition - A Data-Driven Algorithm
The Alan Turing Institute
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10
Yonina Eldar - Model Based Deep Learning with Application to Super Resolution - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM)
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11
Ján Drgoňa - Neuromancer: Differentiable Programming Library for Data-driven Modelling and Control
DataLearning@ICL
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12
Matthieu Darcy - Kernel methods for operator learning
One world theoretical machine learning
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13
DDPS | A flexible and generalizable XAI framework for scientific deep learning
Inside Livermore Lab
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14
George Karniadakis, BINNS: Biophysics-Informed Neural Networks
IBS Biomedical Mathematics Group
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15
Prof. Rebecca Willett | Kirk Lecture: Machine-Learning Enabled Imaging: From Microscopy to Medical..
Isaac Newton Institute for Mathematical Sciences
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16
PINNs for BVP || Holistic view on SciML || Seminar on: January 20, 2023
CRUNCH Group: Home of Math + Machine Learning + X
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17
Distinguished Seminar in Computational Science and Engineering: Ellen Kuhl, 3/23/23
MIT CCSE
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18
Nonlinear dimensionality reduction for unsteady fluid flow modeling - APS DFD 2022
Csala Hunor
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19
Online Weak-form Sparse Identification of Partial Differential Equations
MSML22
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20
Learning Green's Functions of Linear Reaction-Diffusion Equations
MSML22
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21
DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar
Inside Livermore Lab
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22
Dr. Paris Perdikaris -- Supervised and physics-informed learning in function spaces
Chalmers AI4Science
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23
Introduction to deep learning for fluid mechanics
Ricardo Vinuesa
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24
XAI Tutorial: Explainability OF Deep Neural Networks
NCSAatIllinois
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25
Webinar #8: Topology-Informed Biomedical Image Analysis - Prof. Chao Chen
IEEE EMBS Technical Community on BIIP
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26
Miguel Bessa: Cooperative Data-driven Modeling
MICDE University of Michigan
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27
Learning operators using deep neural networks for multiphysics, multiscale, & multifidelity problems
Ben Erichson
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28
DDPS |Scientific Uses of Automatic Differentiation by Michael Brenner
Inside Livermore Lab
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29
PINNs for Shell Structures || Neural Network Intelligence (NNI) || Seminar on: October 7, 2022
CRUNCH Group: Home of Math + Machine Learning + X
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30
Data-driven prediction of dynamical systems from partial observations
Ben Erichson
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31
IFAIME - 2022-08-30 | Keynote Lecture 08: Petros KOUMOUTSAKOS
TuCAI
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32
HLCS | Interpretable and Explainable Data-Driven Methods for Physical Simulations
Inside Livermore Lab
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33
Andrew Stuart - Supervised Learning For Operators
Physics Informed Machine Learning
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34
Exploiting Symmetry in Deep Dynamics Models For Improved Generalization
Ben Erichson
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35
Learning Operators with Coupled Attention
范迪夏
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36
Sparse Nonlinear Models for Fluid Dynamics with Machine Learning and Optimization
Steve Brunton
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37
Fast reduction of nonlinear finite-element models to spectral submanifolds. Prof. George Haller
NODYCON Conference Series
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38
Nathan Kutz: The Future of Governing Equations
Oxford ML and Physics Seminars
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39
DDPS | The problem with deep learning for physics (and how to fix it) by Miles Cranmer
Inside Livermore Lab
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40
Neural Implicit Flow
范迪夏
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41
AFMS Webinar 2021 #28 - Dr Hessam Babaee (University of Pittsburgh)
Australasian Fluid Mechanics Society Inc
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42
Building and deploying CNN based solvers
RocketML
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43
Jane Bae - Wall-models of turbulent flows via scientific multi-agent reinforcement learning
Physics Informed Machine Learning
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44
Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models
Steve Brunton
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45
Matthias Ehrhardt - Bilevel Learning for Inverse Problems
One world theoretical machine learning
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46
Introduction to Analytic Foundations of Deep Learning & Foundations of Feedforward Networks: Part I
C3 Digital Transformation Institute
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47
MCF2021 - Stefania FRESCA, MOX Politecnico di Milano (IT)
Laboratorio MOX
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48
Jin Keun Seo: "Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Im..."
Institute for Pure & Applied Mathematics (IPAM)
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49
Tom Goldstein: "An empirical look at generalization in neural nets"
Institute for Pure & Applied Mathematics (IPAM)
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50
Mikhail Belkin: "Optimization for over-parameterized systems of non-linear equations"
Institute for Pure & Applied Mathematics (IPAM)
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51
Targeted use of deep learning for physics-informed model discovery by Nathan Kutz
MLPS - Combining AI and ML with Physics Sciences
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52
George Karniadakis: Data-Centric Engineering Webinar Series
Cambridge University Press
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53
Accurate reconstruction of flow trajectories from incomplete data of turbulence
Fluid Dynamics Seminars Imperial College London
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54
An introduction to machine learning for fluid dynamics - Peter Jimack
Leeds Institute for Fluid Dynamics
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55
Rene Vidal - Keynote: Mathematics of Deep Learning
DeepMath
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56
Advancing Reacting Flow Simulations with Data-Driven Models: (Prof. Alessandro Parente)
von Karman Institute for Fluid Dynamics
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57
Demba Ba - Deeply-Sparse Signal Representations
DeepMath
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58
Nonlinear Model Reduction: Using ML to Enable Rapid Simulation of Extreme-Scale Physics Models
MLPS - Combining AI and ML with Physics Sciences
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59
Methods for System Identification (Prof. Steve L. Brunton)
von Karman Institute for Fluid Dynamics
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60
Lars Ruthotto - Machine Learning vs Optimal Transport: Old solutions for new problems and vice versa
FAU Applied Mathematics
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61
01 - Introduction to Machine Learning - Brenda Ng
NERSC
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62
Steve Brunton: Machine Learning for Fluid Dynamics
LLMs Explained - Aggregate Intellect - AI.SCIENCE
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63
AN20: Partial Differential Equations Meet Deep Learning: Old Solutions for New Problems & Vice Versa
SIAM Conferences
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64
MDS20 Minitutorial: Learning to Solve Inverse Problems in Imaging by Rebecca Willett
SIAM Conferences
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65
MDS20 Minitutorial: ODE/PDE Neural Networks by Eldad Haber
SIAM Conferences
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66
Week 1 – Lecture: History, motivation, and evolution of Deep Learning
Alfredo Canziani
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67
Topology-Informed Machine Learning to Predict Glass Properties
Mathieu Bauchy
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68
DeepXDE: A Deep Learning Library for Solving Differential Equations by Lu Lu
MLPS - Combining AI and ML with Physics Sciences
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69
MSML2020 Invited Talk by Prof. George Karniadakis, Brown University
MSML2020Conference
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70
MSML2020 Invited Talk by Prof. Nathan Kutz, University of Washington
MSML2020Conference
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