Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
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Updated
Dec 4, 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.
A Physics-Informed Neural Network to solve 2D steady-state heat equations.
Applications of PINOs
No need to train, he's a smooth operator
Learning function operators with neural networks.
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks
A Physics-informed neural network (PINN) library.
Supporting code for "reduced order modeling using advection-aware autoencoders"
Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Physics Informed Neural Networks - research in problem solving, architecture improvements, new applications
Hidden physics models: Machine learning of nonlinear partial differential equations
Deep learning library for solving differential equations and more
Includes codes for, "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning"
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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