Physics-informed neural networks for scientific modeling: uses, implementations, and directions

Alexander New; Andrew S. Gearhart; Ryan A. Darragh; Marisel Villafañe-Delgado

doi:10.1117/12.3013520

7 June 2024 Physics-informed neural networks for scientific modeling: uses, implementations, and directions

Alexander New, Andrew S. Gearhart, Ryan A. Darragh, Marisel Villafañe-Delgado

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130511J (2024) https://doi.org/10.1117/12.3013520
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Physics-informed neural networks (PINNs) are a recently-developed scientific machine learning (SciML) approach useful for predicting behavior of systems governed by differential equations (DEs). Compared to classical methods like finite element method (FEM), PINNs can be easily set up and trained on general DEs and geometries. In this work, we will discuss uses of PINNs in different scientific domains. Our focus will be on the use of pinn-jax, an open-source library we have designed to enable easy development and training of PINNs on varied problems, including forward prediction and inverse estimation. We have designed pinn-jax to be easily extensible while also featuring implementations of some common techniques for enhancing PINNs, and we will demonstrate these on different problems. Particular attention will be paid to evaluating PINNs’ performance on problems that vary in behavior across different temporal and spatial scales.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Alexander New, Andrew S. Gearhart, Ryan A. Darragh, and Marisel Villafañe-Delgado "Physics-informed neural networks for scientific modeling: uses, implementations, and directions", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130511J (7 June 2024); https://doi.org/10.1117/12.3013520

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