Alternatives and similar repositories for GP-NODEs

Users that are interested in GP-NODEs are comparing it to the libraries listed below

• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆54Updated last year
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆57Updated 3 years ago
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆76Updated 2 months ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 7 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆61Updated this week
• niallmm / Hybrid-SINDy
  Sparse Identification of Nonlinear Dynamics for Hybrid Systems
  ☆25Updated 7 years ago
• PredictiveIntelligenceLab / BayesianDifferentiableProgramming
  ☆21Updated 5 years ago
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆24Updated 5 years ago
• luckystarufo / pySINDy
  SINDy (Sparse Identification of Nonlinear Dynamics) algorithms
  ☆79Updated 2 years ago
• kpchamp / MultiscaleDiscovery
  ☆30Updated 7 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• paulxshen / neural-ode-cartpole
  ☆24Updated 5 years ago
• IvanYashchuk / jax-fenics
  Differentiable interface to FEniCS for JAX
  ☆58Updated 4 years ago
• rezaakb / pinns-jax
  PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
  ☆54Updated last year
• andreikitaitsev / SymINDy
  Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…
  ☆76Updated 3 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆47Updated last year
• william-redman / Koopman-Neural-Network-Training
  Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…
  ☆21Updated 2 years ago
• wzhi / ODElearning_INN
  [ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks
  ☆10Updated 2 years ago
• loiseaujc / SINDy
  ☆41Updated 7 years ago
• briandesilva / discovery-of-physics-from-data
  Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"
  ☆28Updated 5 years ago
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆84Updated 2 weeks ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆111Updated 4 years ago
• marvinpfoertner / linpde-gp
  Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
  ☆30Updated last year
• MColbrook / Residual-Dynamic-Mode-Decomposition
  Code for ResDMD: data-driven spectral properties of Koopman Operators
  ☆38Updated last year
• clegaard / deep_learning_for_dynamical_systems
  ☆89Updated 2 years ago
• dmusekamp / al4pde
  AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers
  ☆29Updated 4 months ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 3 years ago
• cselab / LED
  ☆21Updated 2 years ago