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 last year
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…☆13Updated 6 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…☆52Updated 2 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆25Updated 3 years ago
- Dynamic weight strategy of physics-informed neural networks for the 2D Navier-Stokes equations☆14Updated 3 years ago
- ☆13Updated last year
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆16Updated last year
- Bayesian PINN codes to solve 2D/3D Navier Stokes for wind fields☆10Updated 2 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆28Updated 4 years ago
- Implementing physics informed neural networks (PINN) in PyTorch to solve turbulent flows using the Navier-Stokes equations☆26Updated last year
- ☆17Updated 2 years ago
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long sho…☆39Updated last year
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 4 years ago
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆20Updated 4 years ago
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago
- A Surrogate Model with Data Augmentation and Deep Transfer Learning for Temperature Field Prediction of Heat Source Layout☆10Updated 5 years ago
- Tensoflow 2 implementation of physics informed deep learning.☆27Updated 5 years ago
- Paper List of Physics-Informed Neural Network (PINN)☆51Updated last week
- ☆18Updated last year
- ☆10Updated 4 years ago
- ☆21Updated 2 years ago
- ☆19Updated 3 years ago
- An automatic knowledge embedding framework for scientific machine learning☆23Updated 3 years ago
- The repository contains implementations of examples provided in the literature on energy minimization based approach to Physics Informed …☆11Updated 5 years ago
- ☆14Updated 3 years ago
- Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used …☆34Updated 4 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆33Updated 2 years ago
- Sample codes for training of Voronoi-tessellation-assisted convolutional neural network by Fukami et al. (Nature Machine Intelligence 202…☆57Updated 2 years ago
- 采用PINN/ResPINN对两种偏微分方程（Burgers&Allen-Cahn）的训练与求解☆16Updated 4 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…☆27Updated 7 months ago