JAX, M.D.: A Framework for Differentiable Physics

9 December 2019

Papers citing "JAX, M.D.: A Framework for Differentiable Physics"

8 / 8 papers shown

Title
Exploring Physical Latent Spaces for High-Resolution Flow Restoration Chloé Paliard Nils Thuerey Kiwon Um AI4CE 31 0 0 21 Nov 2022
Learn2Hop: Learned Optimization on Rough Landscapes Amil Merchant Luke Metz S. Schoenholz E. D. Cubuk 31 16 0 20 Jul 2021
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots Kun Wang Mridul Aanjaneya Kostas Bekris 27 15 0 10 Nov 2020
End-to-End Differentiable Molecular Mechanics Force Field Construction Yuanqing Wang Josh Fass Benjamin Kaminow John E. Herr Dominic Rufa Ivy Zhang Iván Pulido Mike Henry J. Chodera 32 23 0 02 Oct 2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Li Li Stephan Hoyer Ryan Pederson Ruoxi Sun E. D. Cubuk Patrick F. Riley K. Burke AI4CE 37 120 0 17 Sep 2020
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines Kun Wang Mridul Aanjaneya Kostas Bekris PINN 15 21 0 28 Apr 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 111 49 0 27 Feb 2020
Learning to Simulate Complex Physics with Graph Networks Alvaro Sanchez-Gonzalez Jonathan Godwin Tobias Pfaff Rex Ying J. Leskovec Peter W. Battaglia PINN AI4CE 58 1,056 0 21 Feb 2020