iHealth workshop about applications of physics-informed neural networks (PINNs)

Implementação de metodologias baseadas em redes neurais para a solução de equações diferenciais parciais.

A pytorch framework for solving PDEs via Physics Informed Neural Networks (PINNs)!

Course HW Submissions for Advanced Topics in Scientific Machine Learning

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Programmes Python et C++ de mon projet tuteuré de S6. (2024)

This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.

Improving hyperthermia treatment by controlling temperature distribution in both 1D and 2D domains and thermal energy applied to cutaneous and subcutaneous tissues, through Bio-Heat equation with Physics-Informed Neural Networks (PINNs).

A Python General-Purpose Implementation For Physics Informed Neural Networks

Simple example of PINNs usage

Code for the project on physics informed neural networks for the course special topics in computational science and engineering.

Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.

Comparative analysis of computational modeling of acoustic wave propagation using neural network-based methods.

Fitting 2D curves or Multi-variable partial Differential Equations

Solve 2D Navier-Stokes Equation using Physics-Informed Network

A repository to learn about Physics-informed Neural Networks!

C++ automatic differentiation library with no dependencies and arbitrary higher order derivatives, stand-alone, header only

This repository contains the source code for the paper: "PINNing Cerebral Blood Flow: Analysis of Perfusion MRI in Infants using Physics-Informed Neural Networks"

Python PINNs package to solve Electron Transfer Models. Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

UQ Group (Director: Hadi Meidani)