1 code implementation • 28 Apr 2023 • Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis
We find that existing differentiable simulation methods provide inaccurate gradients when the contact normal direction is not fixed - a general situation when the contacts are between two moving objects.
no code implementations • 12 Dec 2022 • Yaofeng Desmond Zhong, Tongtao Zhang, Amit Chakraborty, Biswadip Dey
Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks.
1 code implementation • 8 Jul 2022 • Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis
In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator, Nimble Physics, diffTaichi, Brax, Warp, Dojo and DiffCoSim.
no code implementations • 30 Oct 2021 • Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network.
no code implementations • 12 Sep 2021 • Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network.
1 code implementation • NeurIPS 2021 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this paper, we introduce a differentiable contact model, which can capture contact mechanics: frictionless/frictional, as well as elastic/inelastic.
no code implementations • 3 Dec 2020 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks.
1 code implementation • NeurIPS 2020 • Yaofeng Desmond Zhong, Naomi Ehrich Leonard
The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories.
1 code implementation • ICLR 2020 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.