Physics-aware-AI / Symplectic-ODENet

Related projects ⓘ

Alternatives and complementary repositories for Symplectic-ODENet

• DesmondZhong / Lagrangian_caVAE
  Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
  ☆17Updated 2 years ago
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆27Updated last year
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆51Updated 8 months ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆39Updated 11 months ago
• coursekevin / weakformghnn
  Accompanying code for "Weak form generalized Hamiltonian learning"
  ☆9Updated 3 years ago
• CampusAI / Hamiltonian-Generative-Networks
  Re-implementation of Hamiltonian Generative Networks paper
  ☆33Updated 2 years ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆102Updated 3 years ago
• fusion-ml / OCBO
  Offline Contextual Bayesian Optimization
  ☆14Updated last year
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 6 years ago
• d-biswa / Symplectic-ODENet
  ☆14Updated 3 years ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆65Updated last year
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆55Updated 2 years ago
• YunzhuLi / CompositionalKoopmanOperators
  [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control
  ☆84Updated 3 years ago
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆44Updated 3 years ago
• cagatayyildiz / oderl
  Experiment code for "Continuous-Time Model-Based Reinforcement Learning"
  ☆47Updated 11 months ago
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆37Updated 5 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆39Updated 3 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆22Updated 11 months ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆72Updated 2 weeks ago
• crispitagorico / torchspde
  Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics
  ☆47Updated last year
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆49Updated 3 years ago
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆22Updated 4 years ago
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆41Updated 3 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆27Updated 2 years ago
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆85Updated 2 years ago
• locuslab / stable_dynamics
  Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)
  ☆32Updated 4 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆47Updated last month
• hegdepashupati / gaussian-process-odes
  Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes
  ☆12Updated 2 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆25Updated 2 years ago
• ManshaP / KoopmanNets
  project for my essay on how to use neural networks to linearise nonlinear dynamical systems
  ☆9Updated 4 years ago