Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
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Updated
Jun 12, 2024 - Python
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Code accompanying my blog post: So, what is a physics-informed neural network?
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Applications of PINOs
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
No need to train, he's a smooth operator
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Learning function operators with neural networks.
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
Supporting code for "reduced order modeling using advection-aware autoencoders"
A Physics-informed neural network (PINN) library.
Physics Informed Neural Networks - research in problem solving, architecture improvements, new applications
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Hidden physics models: Machine learning of nonlinear partial differential equations
Deep learning library for solving differential equations and more
Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127
Π-ML: Learn data-driven similarity theories of physical problems
Physics-based machine learning with dynamic Boltzmann distributions
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