Implementation of CogSci 2019 paper 'Active physical learning via reinforcement learning'
-
Updated
Jun 2, 2021 - Python
Implementation of CogSci 2019 paper 'Active physical learning via reinforcement learning'
IsoGCN code for ICLR2021
An extensible benchmark suite to evaluate data-driven physical simulation
Applications of the Teg differentiable programming language to problems spanning graphics and physical simulation.
[NeurIPS 2022] The implementation for the paper "Learning Physical Dynamics with Subequivariant Graph Neural Networks".
PENN code for NeurIPS 2022
Simulink model and python interface to simulate electrical motor operations.
Code for the paper "Structure-preserving neural networks" published in Journal of Computational Physics (JCP).
Code for the paper "Deep learning of thermodynamics-aware reduced-order models from data" published in Computer Methods in Applied Mechanics and Engineering (CMAME).
Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).
Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
Add a description, image, and links to the physical-simulation topic page so that developers can more easily learn about it.
To associate your repository with the physical-simulation topic, visit your repo's landing page and select "manage topics."