MunzirH / Applications-of-Physics-Informed-Machine-LearningLinks
🌌 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 11 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
Sorting:
- Implementation of physics-informed PointNet (PIPN) for weakly-supervised learning of incompressible flows and thermal fields on irregular…☆11Updated 4 months ago
- ☆14Updated 3 years ago
- ☆13Updated 11 months ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 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…☆50Updated 2 years ago
- ☆10Updated 4 years ago
- Dynamic weight strategy of physics-informed neural networks for the 2D Navier-Stokes equations☆13Updated 3 years ago
- 这是一个基于 Python 有限元理论,算法与应用的仓库☆14Updated 6 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆24Updated 3 years ago
- ☆21Updated 2 years ago
- ☆10Updated 2 years ago
- ☆18Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆30Updated 3 years ago
- Efficiently solve the 2D heat equation using a Physics-Informed Neural Network (PINN). Simulate and predict temperature distributions wit…☆11Updated last year
- 🏔️ PINNACLE: PINN Adaptive ColLocation and Experimental points selection☆24Updated last year
- Official code for AL-PINNS: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks☆11Updated 2 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆26Updated 4 years ago
- ☆18Updated last year
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- This repo contains a PyTorch-based AE-ConvLSTM model for fluid flow prediction. It can forecast 5–10 time steps per forward pass and over…☆25Updated 5 months ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆24Updated 2 years ago
- ☆11Updated 4 years ago
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆15Updated 10 months ago
- ☆27Updated 2 years ago
- Physics-Informed Neural Network, Finite Element Method enhanced neural network, and FEM data-based neural network☆18Updated 8 months ago
- Implementing physics informed neural networks (PINN) in PyTorch to solve turbulent flows using the Navier-Stokes equations☆25Updated last year
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long sho…☆39Updated 11 months ago
- In his project, we proposed a new acquisition function for kriging-based reliability analysis, namely expected uncertainty reduction (EUR…☆10Updated 3 years ago
- Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used …☆34Updated 4 years ago
- Code for Learning Sparse Nonlinear Dynamics via Mixed Integer Optimization☆16Updated 3 years ago