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GINNs: Graph-Informed Neural Networks for multiscale physics by Eric J. Hall; Søren Taverniers; Markos A. Katsoulakis; Daniel M. Tartakovsky is a Engineering article available to read on EtoBox.

What is GINNs: Graph-Informed Neural Networks for multiscale physics about?

We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems. GINNs address the twin challenges of removing intrinsic computational bottlenecks in physicsbased models and generating large data sets for estimating probability distributions of quantities of interest (QoIs) with a high degree of confidence. Both the selection of the complex physics learned by the NN and its supervised learning/prediction are informed by a PGM, which includes the formulation of structured priors for tunable control variables (CVs) to account for their mutual correlations and ensure physically sound CV and QoI distributions. GINNs accelerate the prediction of QoIs essential for simulation-based decision-making where generating sufficient sample data using physics-based models alone is often prohibitively expensive. Using applications grounded in energy storage, we describe the construction of GINNs for a domain-aware Bayesian network that embeds a homogenized model of supercapacitor dynamics and a data-driven Bayesia

Who reads GINNs: Graph-Informed Neural Networks for multiscale physics?

Author

Eric J. Hall; Søren Taverniers; Markos A. Katsoulakis; Daniel M. Tartakovsky

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Engineering (Physical Sciences)