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 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
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.β25Updated 3 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform toβ¦β46Updated 2 years ago
- β13Updated 3 months ago
- β10Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEsβ31Updated 3 years ago
- Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorchβ36Updated 5 months ago
- β14Updated 3 years ago
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"β21Updated 4 years ago
- Implementing physics informed neural networks (PINN) in PyTorch to solve turbulent flows using the Navier-Stokes equationsβ21Updated 10 months ago
- AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)β42Updated 2 years ago
- Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equβ¦β14Updated 2 years ago
- β12Updated 9 months ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networksβ34Updated 3 years ago
- This repository introduces Partial Differential Equation Solver using neural network that can learn resolution-invariant solution operatoβ¦β16Updated 3 years ago
- Physics-informed neural networks (PINNs)β11Updated 2 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimizationβ19Updated 2 years ago
- β24Updated last year
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long shoβ¦β10Updated 4 months ago
- Laminar flow prediction using graph neural networksβ27Updated 2 months ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.β25Updated 3 years ago
- Stochastic Physics-Informed Neural Ordinary Differential Equationsβ16Updated 2 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNsβ24Updated last year
- Yet another PINN implementationβ20Updated 9 months ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"β17Updated 2 years ago
- β22Updated 2 years ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weightingβ27Updated last year
- Tensoflow 2 implementation of physics informed deep learning.β27Updated 4 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systemsβ63Updated 5 years ago
- Research/development of physics-informed neural networks for dynamic systemsβ18Updated 3 months ago