Alternatives and similar repositories for Symplectic-ODENet

Users that are interested in Symplectic-ODENet are comparing it to the libraries listed below

• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆57Updated 2 years ago
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆28Updated 2 years ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆110Updated 4 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆47Updated last year
• YunzhuLi / CompositionalKoopmanOperators
  [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control
  ☆90Updated 4 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆54Updated last year
• locuslab / stable_dynamics
  Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)
  ☆33Updated 5 years ago
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆24Updated 5 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆47Updated 3 years ago
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆90Updated 3 years ago
• fusion-ml / OCBO
  Offline Contextual Bayesian Optimization
  ☆14Updated 2 years ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆129Updated last year
• CampusAI / Hamiltonian-Generative-Networks
  Re-implementation of Hamiltonian Generative Networks paper
  ☆33Updated 3 years ago
• cagatayyildiz / oderl
  Experiment code for "Continuous-Time Model-Based Reinforcement Learning"
  ☆54Updated last year
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 7 years ago
• lukebhan / PDEControlGym
  This gym provides implementations of various PDEs for easy testing and comparison of data-driven and classical PDE control algorithms.
  ☆30Updated last month
• DesmondZhong / Lagrangian_caVAE
  Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
  ☆19Updated 2 years ago
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆51Updated 3 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆27Updated 2 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆42Updated 3 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆30Updated last year
• ghliu / snopt
  Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight
  ☆53Updated 3 years ago
• a-norcliffe / sonode
  Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"
  ☆59Updated 10 months ago
• ivandariojr / LyapunovLearning
  ☆43Updated 2 years ago
• martius-lab / CombOptNet
  Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}
  ☆72Updated 3 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• spbu-math-cs / Riemannian-Gaussian-Processes
  Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".
  ☆29Updated 6 months ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆52Updated 3 years ago