Learning to Control PDEs with Differentiable Physics

International Conference on Learning Representations (ICLR), 2020

21 January 2020

Papers citing "Learning to Control PDEs with Differentiable Physics"

24 / 124 papers shown

Title
A Latent space solver for PDE generalization Rishikesh Ranade C. Hill Haiyang He Amir Maleki Jay Pathak AI4CE 113 4 0 06 Apr 2021
DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape InterpolationACM Transactions on Graphics (TOG), 2021 Pingchuan Ma Tao Du John Z. Zhang Kui Wu Andrew Spielberg Robert K. Katzschmann Wojciech Matusik 150 40 0 02 Apr 2021
Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact Keenon Werling Dalton Omens Jeongseok Lee Ionnis Exarchos Chenxi Liu PINN 212 85 0 30 Mar 2021
Learning optimal multigrid smoothers via neural networksSIAM Journal on Scientific Computing (SISC), 2021 Ru Huang Ruipeng Li Yuanzhe Xi AI4CE 143 33 0 24 Feb 2021
NTopo: Mesh-free Topology Optimization using Implicit Neural RepresentationsNeural Information Processing Systems (NeurIPS), 2021 Jonas Zehnder Yue Li Stelian Coros B. Thomaszewski AI4CE 136 70 0 22 Feb 2021
Neural Particle Image Velocimetry N. Stulov Michael Chertkov 73 7 0 28 Jan 2021
DiffPD: Differentiable Projective DynamicsACM Transactions on Graphics (TOG), 2021 Tao Du Kui Wu Pingchuan Ma Sebastien Wah Andrew Spielberg Daniela Rus Wojciech Matusik 210 119 0 15 Jan 2021
How to Train Your Differentiable FilterAutonomous Robots (Auton. Robots), 2020 Alina Kloss Georg Martius Jeannette Bohg 262 49 0 28 Dec 2020
Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3DThe Physics of Fluids (Phys. Fluids), 2020 Nils Wandel Michael Weinmann Reinhard Klein AI4CE 193 57 0 22 Dec 2020
On The Verification of Neural ODEs with Stochastic GuaranteesAAAI Conference on Artificial Intelligence (AAAI), 2020 Sophie Gruenbacher Ramin Hasani Mathias Lechner J. Cyranka S. Smolka Radu Grosu 139 37 0 16 Dec 2020
DPM: A Novel Training Method for Physics-Informed Neural Networks in ExtrapolationAAAI Conference on Artificial Intelligence (AAAI), 2020 Jungeun Kim Kookjin Lee Dongeun Lee Sheo Yon Jin Noseong Park PINN AI4CE 82 102 0 04 Dec 2020
Scalable Differentiable Physics for Learning and Control Yi-Ling Qiao Junbang Liang V. Koltun Ming Lin PINN AI4CE 167 128 0 04 Jul 2020
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact Moritz Geilinger D. Hahn Jonas Zehnder M. Bacher B. Thomaszewski Stelian Coros AI4CE 118 39 0 02 Jul 2020
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Kiwon Um R. Brand Yun Fei Fei Philipp Holl N. Thürey AI4CE 223 303 0 30 Jun 2020
Learning Physical Constraints with Neural ProjectionsNeural Information Processing Systems (NeurIPS), 2020 Shuqi Yang Xingzhe He Bo Zhu 3DV AI4CE 214 30 0 23 Jun 2020
Neural Ordinary Differential Equation Control of Dynamics on Graphs Thomas Asikis Lucas Böttcher Nino Antulov-Fantulin 181 48 0 17 Jun 2020
Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize Nils Wandel Michael Weinmann Reinhard Klein AI4CE 157 9 0 15 Jun 2020
Liquid Time-constant Networks Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus Radu Grosu AI4TS AI4CE 156 292 0 08 Jun 2020
Learning Long-Term Dependencies in Irregularly-Sampled Time Series Mathias Lechner Ramin Hasani AI4TS 250 149 0 08 Jun 2020
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics S. Xiong Xingzhe He Yunjin Tong Yitong Deng Bo Zhu 207 10 0 07 Jun 2020
Lagrangian Neural Style Transfer for FluidsACM Transactions on Graphics (TOG), 2020 Byungsoo Kim Vinicius Azevedo Markus Gross B. Solenthaler 175 42 0 02 May 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 278 51 0 27 Feb 2020
Learning to Simulate Complex Physics with Graph NetworksInternational Conference on Machine Learning (ICML), 2020 Alvaro Sanchez-Gonzalez Jonathan Godwin Tobias Pfaff Rex Ying J. Leskovec Peter W. Battaglia PINN AI4CE 356 1,255 0 21 Feb 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorsNature Machine Intelligence (NMI), 2019 Lu Lu Pengzhan Jin George Karniadakis 809 2,661 0 08 Oct 2019