Alternatives and similar repositories for deep_learning_for_dynamical_systems

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

• GaloisInc / dlkoopman
  A general-purpose Python package for Koopman theory using deep learning.
  ☆104Updated 5 months ago
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆74Updated 2 months ago
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆80Updated 2 months ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆74Updated 2 years ago
• loiseaujc / SINDy
  ☆41Updated 7 years ago
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆57Updated 2 years ago
• haozhg / oml
  AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
  ☆30Updated 2 years ago
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆56Updated 3 years ago
• haller-group / SSMLearn
  Data-driven reduced order modeling for nonlinear dynamical systems
  ☆84Updated last month
• dynamicslab / pysensors
  PySensors is a Python package for sparse sensor placement
  ☆92Updated last week
• mbudisic / koopman
  Koopman Mode Decomposition
  ☆71Updated 7 years ago
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆96Updated 3 years ago
• ContiPaolo / EKF-SINDy
  Empowering extended Kalman filter (EKF) with Sparse Identification of Nonlinear Dynamics (SINDy)
  ☆57Updated 2 months ago
• dynamicslab / hydrogym
  An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.
  ☆75Updated 3 weeks ago
• katiana22 / GDM-PCE
  Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
  ☆37Updated 3 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆46Updated last year
• dynamicslab / SINDy-PI
  SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics
  ☆149Updated 3 years ago
• drgona / PIML_ACC2023
  Material for the tutorial on "Physics-Informed Machine Learning (PIML) for Modeling and Control of Dynamical Systems" presented at the Am…
  ☆19Updated last year
• EthanJamesLew / AutoKoopman
  AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
  ☆76Updated last year
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• haozhg / odmd
  AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)
  ☆43Updated 2 years ago
• dynamicslab / dominant-balance
  Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)
  ☆57Updated 4 years ago
• helange23 / from_fourier_to_koopman
  Linear and non-linear spectral forecasting algorithms
  ☆137Updated 4 years ago
• hyumo / pysindy-gui
  PySINDy GUI
  ☆39Updated 2 years ago
• luckystarufo / pySINDy
  SINDy (Sparse Identification of Nonlinear Dynamics) algorithms
  ☆78Updated 2 years ago
• eurika-kaiser / KRONIC
  Koopman Reduced-Order Nonlinear Identification and Control
  ☆90Updated 5 years ago
• dynamicslab / pykoopman
  A package for computing data-driven approximations to the Koopman operator.
  ☆367Updated 8 months ago
• yriyazi / Koopman-Operator-and-Deep-Neural-Networks-ISAV2023
  In this work, we present a novel approach that combines the power of Koopman operators and deep neural networks to generate a linear rep…
  ☆10Updated last year
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆49Updated 3 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆60Updated 7 months ago