Alternatives and similar repositories for from_fourier_to_koopman:

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

• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆71Updated last year
• luckystarufo / pySINDy
  SINDy (Sparse Identification of Nonlinear Dynamics) algorithms
  ☆74Updated 2 years ago
• mlech26l / ode-lstms
  Code repository of the paper Learning Long-Term Dependencies in Irregularly-Sampled Time Series
  ☆113Updated last year
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆90Updated 3 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆45Updated 4 years ago
• luckystarufo / multiscale_HiTS
  ☆72Updated 4 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• clegaard / deep_learning_for_dynamical_systems
  ☆86Updated 2 years ago
• baddoo / LANDO
  Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)
  ☆28Updated 3 years ago
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆89Updated 3 years ago
• JayLago / DLDMD
  Deep learning assisted dynamic mode decomposition
  ☆19Updated 3 years ago
• josephbakarji / deep-delay-autoencoder
  Discovers high dimensional models from 1D data using deep delay autoencoders
  ☆34Updated 2 years ago
• patrick-kidger / torchcde
  Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
  ☆433Updated last year
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆54Updated 4 months ago
• jacobjinkelly / easy-neural-ode
  Code for the paper "Learning Differential Equations that are Easy to Solve"
  ☆278Updated 3 years ago
• n-takeishi / learning-kis
  Demo implementation of Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
  ☆40Updated 3 years ago
• loiseaujc / SINDy
  ☆41Updated 7 years ago
• tonyduan / mixture-density-network
  Mixture density network implemented in PyTorch.
  ☆141Updated last year
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆94Updated 3 years ago
• GaloisInc / dlkoopman
  A general-purpose Python package for Koopman theory using deep learning.
  ☆97Updated last month
• 000Justin000 / torchdiffeq
  Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
  ☆58Updated 4 years ago
• AlexTMallen / koopman-forecasting
  Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory
  ☆34Updated 3 years ago
• HerreraKrachTeichmann / NJODE
  code for "Neural Jump Ordinary Differential Equations"
  ☆29Updated 2 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆72Updated 2 years ago
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆169Updated 3 years ago
• BorealisAI / continuous-time-flow-process
  PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
  ☆48Updated 4 years ago
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆69Updated 4 months ago
• dynamicslab / pykoopman
  A package for computing data-driven approximations to the Koopman operator.
  ☆342Updated 4 months ago
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆37Updated 5 years ago
• BethanyL / DeepKoopman
  neural networks to learn Koopman eigenfunctions
  ☆399Updated last year