Hidden physics models: Machine learning of nonlinear partial differential equations
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Updated
May 2, 2018 - MATLAB
Hidden physics models: Machine learning of nonlinear partial differential equations
Data-driven Reynolds stress modeling with physics-informed machine learning
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
MeshfreeFlowNet: Physical Constrained Space Time Super-Resolution
Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
Deep learning library for solving differential equations and more
Deep learning for Engineers - Physics Informed Deep Learning
Physics-based machine learning with dynamic Boltzmann distributions
Code accompanying my blog post: So, what is a physics-informed neural network?
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
Supporting code for "reduced order modeling using advection-aware autoencoders"
Applications of PINOs
Physics Informed Neural Networks - research in problem solving, architecture improvements, new applications
All the projects and assignments from HPC-AI specialisation 2022-23
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Π-ML: Learn data-driven similarity theories of physical problems
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
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