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Found 10 papers, 6 papers with code
Improving Gradient Computation for Differentiable Physics Simulation with Contacts
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.
A Neural ODE Interpretation of Transformer Layers
Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks.
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
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.
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
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.
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network.
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
In this paper, we introduce a differentiable contact model, which can capture contact mechanics: frictionless/frictional, as well as elastic/inelastic.
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks.
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane.
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
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.
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
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.
