Graph-Informed Neural Networks

Søren Taverniers, Eric Joseph Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky

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Abstract

Graph-Informed Neural Networks (GINNs) present a strategy for incorporating domain knowledge into scientific machine learning for complex physical systems. The construction utilizes probabilistic graphical models (PGMs) to incorporate expert knowledge, available data, constraints, etc. with physics-based models such as systems of ordinary and partial differential equations (ODEs and PDEs). Computationally intensive nodes in this hybrid model are replaced by the hidden nodes of a neural network (i.e., learned features). Once trained, the resulting GINN surrogate can cheaply generate physically-relevant predictions at scale thereby enabling robust sensitivity analysis and uncertainty quantification (UQ). As proof of concept, we build a GINN for a multiscale model of electrical double-layer capacitor dynamics embedded into a Bayesian network (BN) PDE hybrid model.
Original languageEnglish
Publication statusPublished - Mar 2021
EventAAAI 2021 Spring Symposium Series: Combining Artificial Intelligence and Machine Learning with Physics Sciences - Stanford University (virtual), Palo Alto, United States
Duration: 22 Mar 202124 Mar 2021
https://sites.google.com/view/aaai-mlps

Conference

ConferenceAAAI 2021 Spring Symposium Series
Abbreviated titleAAAI-MLPS 2021
Country/TerritoryUnited States
CityPalo Alto
Period22/03/2124/03/21
Internet address

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