Alternatives and similar repositories for NystromKoopman

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

• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆63Updated this week
• sdascoli / odeformer
  ☆67Updated last year
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆74Updated 2 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆81Updated 6 months ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years ago
• yucenli / bnn-bo
  Bayesian Neural Network Surrogates for Bayesian Optimization
  ☆62Updated last year
• crispitagorico / sigkernel
  Differentiable computations for the signature-PDE-kernel on CPU and GPU.
  ☆55Updated last year
• PredictiveIntelligenceLab / GP-NODEs
  ☆28Updated 3 years ago
• long-da / A-United-Framework-to-Integrate-Physics-into-Gaussian-Processes
  ☆11Updated 2 years ago
• cagrell / gp_constr
  Python model for constrained GP
  ☆26Updated 4 years ago
• fusion-ml / OCBO
  Offline Contextual Bayesian Optimization
  ☆14Updated 2 years ago
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆170Updated 3 years ago
• VectorInstitute / vbll
  Simple (and cheap!) neural network uncertainty estimation
  ☆75Updated 2 weeks ago
• hegdepashupati / gaussian-process-odes
  Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes
  ☆13Updated 3 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆48Updated last year
• anh-tong / signax
  Signax: Signature computation in JAX
  ☆36Updated 9 months ago
• BruntonUWBio / STIMD
  ☆11Updated 7 years ago
• arotem3 / random-kitchen-sinks
  Implementations of various random kitchen sinks algorithms.
  ☆13Updated 5 years ago
• hvarfner / vanilla_bo_in_highdim
  ☆30Updated 8 months ago
• DurstewitzLab / ChaosRNN
  ☆21Updated 2 years ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆38Updated 3 years ago
• 000Justin000 / torchdiffeq
  Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
  ☆63Updated 4 years ago
• dmusekamp / al4pde
  AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers
  ☆29Updated 4 months ago
• arbabiha / StochasticModelingwData
  Data-driven modeling of chaotic systems in the form of SDEs and nonlinear observation maps
  ☆19Updated 5 years ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• 1sa014kawa / JetDMD
  ☆10Updated 2 months ago
• tgcsaba / GPSig
  Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
  ☆48Updated 2 years ago
• BorealisAI / continuous-time-flow-process
  PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
  ☆48Updated 4 years ago