Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.

Investigating PINNs

PyTorch Implementation of Physics-informed Neural Networks

Code accompanying my blog post: So, what is a physics-informed neural network?

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

State of Health (SoH) and Remaining Useful Life (RUL) prediction for Li-ion batteries based on Physics-Informed Neural Networks (PINN).

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

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IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

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Pinneaple is an open-source Physics AI toolkit for Physics-Informed Neural Networks (PINNs), scientific ML, geometry processing, solvers, and reproducible training pipelines.

Physics Informed Neural Network (PINN) for the wave equation.

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Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.

PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

DeepXDE and PINN

Introductory workshop on PINNs using the harmonic oscillator

Characterizing possible failure modes in physics-informed neural networks.

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.

PINN program for computational mechanics

PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations

Tutorials for Physics-Informed Neural Networks