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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