gaoliyao / BayesianSindyAutoencoderLinks
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal Society A.
☆11Updated last year
Alternatives and similar repositories for BayesianSindyAutoencoder
Users that are interested in BayesianSindyAutoencoder are comparing it to the libraries listed below
Sorting:
- ☆10Updated 2 years ago
- SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis☆12Updated 7 months ago
- ☆14Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Research/development of physics-informed neural networks for dynamic systems☆32Updated last year
- combination of sparse identification of nonlinear dynamics with Akaike information criteria☆16Updated 8 years ago
- Material for the tutorial on "Physics-Informed Machine Learning (PIML) for Modeling and Control of Dynamical Systems" presented at the Am…☆19Updated last year
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆59Updated 3 years ago
- ☆26Updated 3 years ago
- SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics☆157Updated 4 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 4 years ago
- ☆10Updated 4 years ago
- Code for ResDMD: data-driven spectral properties of Koopman Operators☆40Updated last year
- Optimal Control with PDEs solved by a Differentiable Solver☆13Updated last year
- Solve mass spring damper system with phyics-informed neural networks in MATLAB☆15Updated last year
- ☆45Updated 4 years ago
- Data-driven reduced order modeling for nonlinear dynamical systems☆97Updated last month
- ☆42Updated 7 years ago
- Empowering extended Kalman filter (EKF) with Sparse Identification of Nonlinear Dynamics (SINDy)☆67Updated 7 months ago
- ☆21Updated 5 years ago
- ☆30Updated 5 years ago
- AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.☆82Updated last year
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆51Updated 2 years ago
- A general-purpose Python package for Koopman theory using deep learning.☆116Updated 2 months ago
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆15Updated last year
- ☆12Updated last year
- Stochastic Physics-Informed Neural Ordinary Differential Equations☆19Updated 3 years ago
- ☆19Updated 3 years ago
- Data-driven dynamical systems toolbox.☆78Updated last month
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆18Updated last year