Generate realistic interpolations between turbulent flows with an adversarially-constrained autoencoder
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Updated
Jun 5, 2019 - Jupyter Notebook
Generate realistic interpolations between turbulent flows with an adversarially-constrained autoencoder
A Python General-Purpose Implementation For Physics Informed Neural Networks
[HYU-Mechanical Convergence Engineering] Artifical Intelligence and Deep Learning Lecture Code
Simple example of PINNs usage
PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
Deep Learning Neural Networks to Identify Phlebology Disease
Physics-Informed Neural Networks for solving various Differential Equations and Inverse Problems
My projects that involve and use machine learning, data science and deep learning techniques with solve or observe a specfic use case
Project Portfolio
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.
Solver linear Schrödinger equation in 1D using Physics Informed Neural Network.
This repository contains all the machine learning and deep learning model I have implemented using various frameworks like keras, tensorflow, scikit-learn, pytorch, etc.
This code trains and implements a stochastic physics-informed neural ordinary differential equation (SPINODE) framework on a directed colloidal self-assembly test case.
A pytorch framework for solving PDEs via Physics Informed Neural Networks (PINNs)!
The official respository for noise-aware physics-informed machine learning (nPIML)
A Physics Informed Neural Network made using PyTorch
Machine Learning-based Second-order Analysis of Beam-columns through Physics-Informed Neural Networks
Solving multiphysics inverse problems with Scientific (physics-informed) Machine Learning
Navier-Stokes oil dynamics in a rectangular 3D tank, physics-informed neural network approach
This repository contains the source code and additional resources for the paper "Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models". The paper discusses the challenges of solar wind forecasting and the application of Physics-Informed Neural Networks (PiNNs) to improve prediction accuracy and computational efficiency.
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