Alternatives and similar repositories for HamiltonianNet

Users that are interested in HamiltonianNet are comparing it to the libraries listed below

• steindoringi / Variational_Integrator_Networks
  ☆20Updated 2 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆54Updated last year
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆24Updated 5 years ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆111Updated 4 years ago
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆28Updated 2 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• Physics-aware-AI / DiffCoSim
  By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …
  ☆36Updated 2 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆53Updated 3 years ago
• CampusAI / Hamiltonian-Generative-Networks
  Re-implementation of Hamiltonian Generative Networks paper
  ☆33Updated 3 years ago
• ghliu / snopt
  Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight
  ☆55Updated 3 years ago
• jpzxshi / sympnets
  ☆28Updated 4 years ago
• hegdepashupati / gaussian-process-odes
  Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes
  ☆13Updated 3 years ago
• spbu-math-cs / Riemannian-Gaussian-Processes
  Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".
  ☆29Updated 8 months ago
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆47Updated 3 years ago
• ghliu / DeepGSB
  Deep Generalized Schrödinger Bridge, NeurIPS 2022 Oral
  ☆52Updated 2 years ago
• gd-vae / gd-vae
  Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This …
  ☆33Updated 2 weeks ago
• wzhi / ODElearning_INN
  [ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks
  ☆10Updated 2 years ago
• google-deepmind / dm_hamiltonian_dynamics_suite
  ☆35Updated 3 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆61Updated this week
• ivandariojr / LyapunovLearning
  ☆43Updated 2 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆28Updated 2 years ago
• jejjohnson / uncertain_gps
  Exploring how to to deal with uncertain inputs with gaussian process regression models.
  ☆27Updated 4 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• facebookresearch / morbo
  Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
  ☆61Updated 3 years ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 7 years ago
• facebookresearch / neural-conservation-law
  code for "Neural Conservation Laws A Divergence-Free Perspective".
  ☆40Updated 2 years ago
• facebookresearch / robust_mobo
  Code for "Robust Multi-Objective Bayesian Optimization Under Input Noise"
  ☆55Updated 3 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• wjmaddox / online_gp
  Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"
  ☆64Updated 4 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆31Updated last year