Half-Inverse Gradients for Physical Deep Learning

18 March 2022

Papers citing "Half-Inverse Gradients for Physical Deep Learning"

11 / 11 papers shown

Title
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 48 269 0 30 Jun 2020
JAX, M.D.: A Framework for Differentiable Physics S. Schoenholz E. D. Cubuk 42 37 0 09 Dec 2019
DiffTaichi: Differentiable Programming for Physical Simulation Yuanming Hu Luke Anderson Tzu-Mao Li Qi Sun N. Carr Jonathan Ragan-Kelley F. Durand 59 384 0 01 Oct 2019
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 346 5,081 0 19 Jun 2018
Deep Dynamical Modeling and Control of Unsteady Fluid Flows Jeremy Morton F. Witherden A. Jameson Mykel J. Kochenderfer AI4CE 62 165 0 18 May 2018
Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev H. Ritter David Barber ODL 49 231 0 12 Jun 2017
A simple neural network module for relational reasoning Adam Santoro David Raposo David Barrett Mateusz Malinowski Razvan Pascanu Peter W. Battaglia Timothy Lillicrap GNN NAI 156 1,613 0 05 Jun 2017
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 508 1,407 0 01 Dec 2016
Accelerating Eulerian Fluid Simulation With Convolutional Networks Jonathan Tompson Kristofer Schlachter Pablo Sprechmann Ken Perlin 80 534 0 13 Jul 2016
Revisiting Natural Gradient for Deep Networks Razvan Pascanu Yoshua Bengio ODL 128 388 0 16 Jan 2013
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 132 6,623 0 22 Dec 2012