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
  ☆53Updated last year
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆54Updated 2 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆53Updated 3 months ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 6 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆24Updated last year
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆73Updated 2 weeks ago
• kpchamp / MultiscaleDiscovery
  ☆27Updated 6 years ago
• niallmm / Hybrid-SINDy
  Sparse Identification of Nonlinear Dynamics for Hybrid Systems
  ☆23Updated 6 years ago
• PredictiveIntelligenceLab / BayesianDifferentiableProgramming
  ☆21Updated 4 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆70Updated 4 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• secondmind-labs / markovflow
  A Tensorflow based library for Time Series Modelling with Gaussian Processes
  ☆29Updated 8 months ago
• jejjohnson / uncertain_gps
  Exploring how to to deal with uncertain inputs with gaussian process regression models.
  ☆27Updated 3 years ago
• 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…
  ☆19Updated last year
• pnkraemer / probdiffeq
  Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and cus…
  ☆41Updated 4 months ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆11Updated 3 years ago
• pnkraemer / tornadox
  Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…
  ☆20Updated 7 months ago
• IlyaOrson / Control-NeuralODE
  Neural ODEs as Feedback Policies for Nonlinear Optimal Control (IFAC 2023) https://doi.org/10.1016/j.ifacol.2023.10.1248
  ☆16Updated last year
• paulxshen / neural-ode-cartpole
  ☆23Updated 4 years ago
• IvanYashchuk / jax-fenics
  Differentiable interface to FEniCS for JAX
  ☆53Updated 3 years ago
• dmusekamp / al4pde
  AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers
  ☆17Updated this week
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆68Updated 3 months ago
• RajDandekar / MSML21_BayesianNODE
  ☆36Updated 3 years ago
• Zymrael / PortHamiltonianNN
  Port-Hamiltonian Approach to Neural Network Training
  ☆22Updated 5 years ago
• Ceyron / exponax
  Efficient Differentiable n-d PDE solvers in JAX.
  ☆24Updated 4 months ago
• andreikitaitsev / SymINDy
  Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…
  ☆75Updated 2 years ago
• marvinpfoertner / linpde-gp
  Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
  ☆25Updated 10 months ago
• yifanc96 / NonLinPDEs-GPsolver
  Code for the paper: Solving and Learning Nonlinear PDEs with Gaussian Processes
  ☆36Updated 4 months ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆43Updated last year
• pswpswpsw / SKDMD
  Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems
  ☆28Updated 2 years ago