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
PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
My projects that involve and use machine learning, data science and deep learning techniques with solve or observe a specfic use case
Machine Learning-based Second-order Analysis of Beam-columns through Physics-Informed Neural Networks
The application of a Physics Informed Neural Network on modelling the parameters of a Continuously Stirred Tank Reactor, based on the data generated by a Simulink model.
This repository contains an implementation of Tensorized Physics Informed Neural Networks (TPINNs) for solving physics-based problem
This project used convolutional neural networks to predict the steady-state concentration of 3D porous media, and subsequently calculates the tortuosity. This package includes data generation, processing, training, and post-processing functions. The loss function includes a Laplacian loss, which is a physics-informed loss.
Scientific Learning project on the monodomain equation
Physics-Informed Neural Network, Finite Element Method enhanced neural network, and FEM data-based neural network
An application of PINN to SIR modeling
Experimentation & Results of nvidia-modulus-sym implementation using PINNs
Code of the CVPR 2024 paper "Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models"
A PyTorch library for Physics-Informed Neural Networks (PINNs)
Hydrodynamic image with the artificial lateral line using physics-informed informed neural networks and other proven methods in 2D dipole localization.
neural-network-based ROM testbed using an auto-encoder as an approximation manifold for the state, and an MLP for the reduced residual function
Physics Informed Neural Networks
This repository is the official implementation of [Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight](Energy and Buildings)
PyTorch implementation of Neural Netowrk Differential Equation Plasma Equilibrium Solver.
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