Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.
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Updated
May 9, 2024 - Python
Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.
Official implementation of Conflict-Free Inverse Gradients Method
Comparative analysis of computational modeling of acoustic wave propagation using neural network-based methods.
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Physics Informed Neural Network
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.
PINNs/FDM for 1D/2D heat and burgers equations
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 repository to learn about Physics-informed Neural Networks!
A Python General-Purpose Implementation For Physics Informed Neural Networks
Fitting 2D curves or Multi-variable partial Differential Equations
Solve 2D Navier-Stokes Equation using Physics-Informed Network
Repository for the Deep Learning in Scientific Computing course offered in Spring 2022 at ETH Zürich: Deep learning project with the aim of studying the preliminary design of a thermal energy storage.
Simple example of PINNs usage
Machine learning for state-to-state
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"
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