MunzirH / Applications-of-Physics-Informed-Machine-Learning
π 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.
β11Updated 5 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
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.β25Updated 3 years ago
- β13Updated 4 months ago
- β14Updated 3 years ago
- A Surrogate Model with Data Augmentation and Deep Transfer Learning for Temperature Field Prediction of Heat Source Layoutβ10Updated 4 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimizationβ19Updated 2 years ago
- Physics-informed neural networks (PINNs)β12Updated 2 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform toβ¦β48Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEsβ31Updated 3 years ago
- β24Updated 2 years ago
- β10Updated 4 years ago
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long shoβ¦β15Updated 5 months ago
- This repository introduces Partial Differential Equation Solver using neural network that can learn resolution-invariant solution operatoβ¦β16Updated 3 years ago
- ποΈ PINNACLE: PINN Adaptive ColLocation and Experimental points selectionβ19Updated 9 months ago
- Efficiently solve the 2D heat equation using a Physics-Informed Neural Network (PINN). Simulate and predict temperature distributions witβ¦β9Updated last year
- β11Updated 11 months ago
- This is the official implementation of "Deep Fuzzy Physics-Informed Neural Networks for Forward and Inverse PDE Problems" (Neural Networkβ¦β14Updated last week
- Physics-informed learning of governing equations from scarce dataβ11Updated 4 years ago
- β13Updated 11 months ago
- β11Updated 4 years ago
- Laminar flow prediction using graph neural networksβ28Updated 3 months ago
- β13Updated last year
- β19Updated 2 years ago
- β10Updated 2 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorchβ10Updated 10 months ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networksβ21Updated last year
- Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equβ¦β14Updated 2 years ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.β15Updated last year
- Solving a class of elliptic partial differential equations(PDEs) with multiple scales utilizing Fourier-based mixed physics informed neurβ¦β11Updated last year
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.β25Updated 3 years ago
- Reduced Order Model Predictive Controlβ22Updated 3 years ago