MunzirH / Applications-of-Physics-Informed-Machine-Learning
🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scientific problems by embedding physical laws directly into models. Includes projects on discovering the Burgers equation, using PINNs for PDEs, and employing SINDy for dynamic systems analaysis with sparse data.
☆11Updated 4 months ago
Alternatives and similar repositories for Applications-of-Physics-Informed-Machine-Learning:
Users that are interested in Applications-of-Physics-Informed-Machine-Learning are comparing it to the libraries listed below
- ☆12Updated 10 months ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆19Updated 2 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- ☆10Updated 4 years ago
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long sho…☆12Updated 4 months ago
- ☆13Updated 4 months ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆47Updated 2 years ago
- This repository introduces Partial Differential Equation Solver using neural network that can learn resolution-invariant solution operato…☆16Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Physics-informed neural networks (PINNs)☆12Updated 2 years ago
- ☆14Updated 3 years ago
- AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)☆42Updated 2 years ago
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆13Updated 3 months ago
- ☆19Updated 2 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆24Updated last year
- ☆11Updated 4 years ago
- ☆16Updated 7 months ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆20Updated last year
- ☆22Updated 2 years ago
- Deep learning assisted dynamic mode decomposition☆19Updated 3 years ago
- ☆11Updated 10 months ago
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆37Updated 2 years ago
- Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch☆36Updated 5 months ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆39Updated 2 years ago
- Laminar flow prediction using graph neural networks☆27Updated 2 months ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆25Updated 3 years ago
- ☆24Updated last year
- Reduced Order Model Predictive Control☆22Updated 3 years ago
- This is the official implementation of "Deep Fuzzy Physics-Informed Neural Networks for Forward and Inverse PDE Problems" (Neural Network…☆12Updated 3 months ago
- A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269☆42Updated 6 years ago