Denis-Khimin / OptimalControlPDEAutoDiffLinks
Optimal Control with PDEs solved by a Differentiable Solver
☆14Updated last year
Alternatives and similar repositories for OptimalControlPDEAutoDiff
Users that are interested in OptimalControlPDEAutoDiff are comparing it to the libraries listed below
Sorting:
- This gym provides implementations of various PDEs for easy testing and comparison of data-driven and classical PDE control algorithms.☆39Updated last month
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆18Updated last year
- Supporting codes for the numerical implementations in the paper "Operator inference for non-intrusive model reduction with quadratic mani…☆11Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆32Updated 4 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆62Updated 5 years ago
- Data-driven reduced order modeling for nonlinear dynamical systems☆99Updated 2 weeks ago
- SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics☆161Updated 4 years ago
- Research/development of physics-informed neural networks for dynamic systems☆32Updated last year
- ☆130Updated 3 years ago
- Matlab implementation of online and window dynamic mode decomposition algorithms☆13Updated 4 years ago
- A library for Koopman Neural Operator with Pytorch.☆315Updated last year
- Physics-informed learning of governing equations from scarce data☆167Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆31Updated 7 months ago
- mathLab mirror of Python Dynamic Mode Decomposition☆113Updated 10 months ago
- An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.☆93Updated 2 weeks ago
- MATLAB codes for physics-informed dynamic mode decomposition (piDMD)☆162Updated last year
- Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal S…☆11Updated last year
- Enhancing Dynamic Mode Decomposition using Autoencoder Networks.☆35Updated 4 years ago
- ☆14Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆84Updated 3 years ago
- Examples implementing physics-informed neural networks (PINN) in Pytorch☆84Updated 4 years ago
- ☆110Updated 4 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆52Updated 3 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated last year
- This repository comprises Jupyter Notebooks that serve as supplementary material to the journal article titled "Review of Multifidelity M…☆12Updated 2 years ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆42Updated 9 months ago
- ☆27Updated 4 years ago
- Transformers for modeling physical systems☆147Updated 2 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 4 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆34Updated 2 years ago