447 skills found · Page 2 of 15
LMMMEng / TransXNet[TNNLS 2025] TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
chengjunyan1 / SocioDojoSocioDojo lifelong learning environment for real-society dynamics and hypothesis and proof agent.
autonomousvision / Occupancy FlowThis repository contains the code for the ICCV 2019 paper "Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics"
tyhuang0428 / DreamPhysics[AAAI 2025] DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors
Joshua-Ren / Learning Dynamics LLMNo description available
lululxvi / TutorialsTutorials on deep learning, Python, and dissipative particle dynamics
quanvuong / Handful Of Trials PytorchUnofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"
markovmodel / DeeptimeDeep learning meets molecular dynamics.
gbionics / JaxsimA differentiable physics engine and multibody dynamics library for control and robot learning.
wanxinjin / Pontryagin Differentiable ProgrammingA unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.
mbchang / DynamicsA Compositional Object-Based Approach to Learning Physical Dynamics
Winston-Gu / CooHOI[NeurIPS 2024 Spotlight] CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Yunbo426 / MIMCode release for "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics" (CVPR 2019)
caiyuchen-ustc / Alpha RLOn Predictability of Reinforcement Learning Dynamics for Large Language Models (ICLR 2026)
PingchuanMa / NCLaw[ICML 2023] Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
Jonas-Nicodemus / PINNs Based MPCWe discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
YunzhuLi / DPI Net[ICLR 2019] Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
SciML / NBodySimulator.jlA differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
LeCAR-Lab / AnycarOfficial implementation of "AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility"
ktr-hubrt / MPNOfficial codes of CVPR21 paper: Learning Normal Dynamics in Videos with Meta Prototype Network
