dylewsky / Data_Driven_Science_Python_Demos
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by J. Nathan Kutz and Steven L. Brunton
☆141Updated 3 years ago
Alternatives and similar repositories for Data_Driven_Science_Python_Demos
Users that are interested in Data_Driven_Science_Python_Demos are comparing it to the libraries listed below
Sorting:
- Matlab files with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems…☆291Updated last year
- SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics☆143Updated 3 years ago
- ☆193Updated 9 months ago
- Introductory workshop on PINNs using the harmonic oscillator☆122Updated 2 weeks ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆56Updated 4 years ago
- Empowering extended Kalman filter (EKF) with Sparse Identification of Nonlinear Dynamics (SINDy)☆53Updated 3 weeks ago
- Basic implementation of physics-informed neural networks for solving differential equations☆86Updated 4 months ago
- ETH Zürich Deep Learning in Scientific Computing Master's course 2023☆109Updated 9 months ago
- Example problems in Physics informed neural network in JAX☆80Updated last year
- A Hands-on Introduction to Physics-Informed Neural Networks☆18Updated 2 weeks ago
- mathLab mirror of Python Dynamic Mode Decomposition☆90Updated 2 months ago
- A package for the sparse identification of nonlinear dynamical systems from data☆11Updated 5 years ago
- MATLAB codes for physics-informed dynamic mode decomposition (piDMD)☆146Updated last year
- Supplementary resources for the textbook Engineering Design Optimization by Joaquim R. R. A. Martins and Andrew Ning☆113Updated 3 years ago
- Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems☆112Updated last month
- Data-driven reduced order modeling for nonlinear dynamical systems☆72Updated last month
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆69Updated 2 years ago
- A package for computing data-driven approximations to the Koopman operator.☆352Updated 6 months ago
- PySINDy GUI☆38Updated 2 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆145Updated 5 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆68Updated 4 years ago
- Tutorial on Gaussian Processes☆62Updated 5 years ago
- Tutorials for Physics-Informed Neural Networks☆64Updated 11 months ago
- Koopman Reduced-Order Nonlinear Identification and Control☆89Updated 5 years ago
- PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.☆120Updated 11 months ago
- ☆116Updated 5 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆74Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆55Updated 2 years ago
- An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.☆71Updated 3 weeks ago