Extrality monthly seminars

Up-coming seminars

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Past seminars

Modeling Linear PDE Systems with Probabilistic Machine Learning
December 12, 2023
Markus Lange-Hegermann and Bogdan Raiță
Diffusion Models for Physical Processes
November 9, 2023
James Thornton
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
October 4, 2023
Grégoire Mialon & Quentin Garrido
Physics-Constrained Machine Learning for Turbulence Modeling
September 5, 2023
Miguel Alfonso Mendez
Learning differentiable solvers for systems with hard constraints
June 13, 2023
Geoffrey NEGIAR
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
May 23, 2023
Jae Yong Lee
Neural Field Editing, Manipulation and Learning
April 18, 2023
Peihao WANG
BelNet: Basis Enhanced Learning, a Mesh-free Neural Operator
March 23, 2023
Zecheng ZHANG
A Causal Manifold to Learn Convection PDEs by Reducing Kolmogorov n-width
February 14, 2023
Rambod Mojgani
Accurate and Fast PDE Solvers via Neural Fields
January 9, 2023
Peter Yichen Chen, Postdoc at MIT Computer Science & Artificial Intelligence Lab
Neural Operator: Learning Maps Between Function Spaces
December 12, 2022
Kamyar Azizzadenesheli, Senior Research Scientist, Nvidia Corporation
Composing Partial Differential Equations with Physics-Aware Neural Networks & Inferring Boundary Conditions in Finite Volume Neural Networks
November 3, 2022
Matthias Karlbauer : University of Tubingen & Timothy Praditia : University of Stuttgart
Symmetry Group Equivariant Architectures for Physics & Lorentz Group Equivariant Neural Network for Particle Physics
October 6, 2022
Alexander Bogatskii, Flatiron Research Fellow, Flatiron Institute, Simons Foundation
Shape Optimization using Design-by-Morphing & Bayesian Optimization For Multi-Objective Mixed-Variable Problems
September 15, 2022
Haris Moazam Sheikh, Researcher at the Computational Fluid Dynamics Laboratory
Continuous-Time Meta-Learning with Forward Mode Differentiation
July 5, 2022
Tristan Deleu, Ph.D. candidate at Mila & Université de Montréal, under the supervision of Yoshua Bengio
Message Passing Neural PDE Solvers & Equivariant Message Passing
June 2, 2022
Johannes Brandstetter - Tenure Track Professor at Institute for Machine Learning Johannes Kepler University Linz
Enabling Empirically and Theoretically Sound Algorithmic Alignment
May 24, 2022
Petar Veličković - Research Scientist
Learned Coarse Models for Efficient Turbulence Simulation
April 7, 2022
Kimberly Stachenfeld, Senior Research Scientist - DeepMind
Integrating machine learning with scientific spatial and temporal modeling
March 23, 2022
Aditi S. Krishnapriyan - Lawrence Berkeley National Laboratory, University of California, Berkeley
Improving Generalization for Dynamical Systems Learning
February 8, 2022
Yuan Yin - Sorbonne Université, CNRS, LIP6 and MLIA
Multiwavelet-based Operator Learning for Differential Equations
January 20, 2022
Gaurav Gupta - Ming Hsieh Department of Electrical Engineering, University of Southern California
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
December 7, 2021
Kiwon Um - TCI Telecom Paris, IP Paris & Technical University of Munich
Beltrami Flow and Neural Diffusion on Graphs & GRAND: Graph Neural Diffusion
November 9, 2021
Ben Chamberlain and James Rowbottom - Twitter Inc research group
Stabilizing Equilibrium Models by Jacobian Regularization
October 20, 2021
Shaojie Bai - Carnegie Mellon University
Physics-driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks
September 9, 2021
Hao Ma - Department of Aerospace and Geodesy, Technical University of Munich
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize & Teaching The Incompressible Navier-Stokes Equations To Fast Neural Surrogate Models In 3D
June 30, 2021
Nils Wandel - University of Bonn, Germany
Data-based Approach for Wing Shape Design Optimization
June 4, 2021
Jichao Li - National University of Singapore NUS, Department of Mechanical Engineering
Understanding and mitigating gradient pathologies in physics-informed neural networks
May 25, 2021
Sifan Wang - Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
April 6, 2021
Arvind T. Mohan - Center for Nonlinear Studies Computer, Computational and Statistical Sciences Division - Los Alamos National Laboratory
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
March 1, 2021
Yunzhu Li - Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT)
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
February 4, 2021
Yuying Liu - Department of applied Mathematics, University of Washington
CFDNet: A deep learning-based accelerator for fluid simulations
December 10, 2020
Octavi Obiols-Sales - University of California Irvine
Lifelong Learning and Catastrophic Forgetting
November 25, 2020
Arthur Douillard - LIP6/MLIA & Heuritech
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