A Python General-Purpose Implementation For Physics Informed Neural Networks
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Updated
Jun 28, 2023 - Python
A Python General-Purpose Implementation For Physics Informed Neural Networks
Official implementation of DEQ-MPI: A deep equilibrium reconstruction model for magnetic particle imaging
Solving multiphysics inverse problems with Scientific (physics-informed) Machine Learning
Physics-informed neural networks(PINN) use universal function approximators to integrate knowledge of physical laws described by PDEs into the learning process, addressing data scarcity in biological and engineering systems.
Repositorio con el material para el taller sobre PINNs en MAPI-3 2024
Deep Learning Neural Networks to Identify Phlebology Disease
A pytorch framework for solving PDEs via Physics Informed Neural Networks (PINNs)!
JAX implementation of the Poisson PINN; Just for practice.
Implementation of Fourier Neural Operator from scratch
Code of the CVPR 2024 paper "Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models"
neural-network-based ROM testbed using an auto-encoder as an approximation manifold for the state, and an MLP for the reduced residual function
Solution of 1D and 2D PDEs using Physics Informed Neural Networks (PINNs)
Physics Informed Neural Networks
Numerical solutions of several PDEs using Physics-Informed Neural Networks
En este repositorio pueden encontrarse los códigos utilizados para realizar el trabajo de tesis de maestría.
(partial, unofficial) JAX-implementation for "Physics-informed Neural Networks for shell structures".
The Rheoinformatic lab website
Generate realistic interpolations between turbulent flows with an adversarially-constrained autoencoder
Repository for the research project "Helmholtz--Hodge physics-informed neural networks".
Academic project on evaluation of the effectiveness of Physics-informed Neural Networks (PINNs) to predict various non-Newtonian fluid flows, such as fluids governed by the Cross Power-Law and Carreau-Yasuda models.
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