Physics-Informed Graph Learning

TL;DR

This paper reviews physics-informed graph learning (PIGL), highlighting its framework, benefits, and future challenges, aiming to stimulate further research in integrating physics rules into graph models.

Contribution

It provides a systematic review of PIGL methods within a unified framework, offering insights into current approaches and future research directions.

Findings

Unified framework for graph learning models

Analysis of existing PIGL methods

Discussion of future challenges in PIGL

Abstract

An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and complexity of graph data. The graph learning models suffer from the inability to efficiently learn graph information. In order to indemnify this inefficacy, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which has enormous benefits. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models followed by examining existing PIGL methods in relation to the unified framework. We also discuss several future challenges for PIGL. This survey paper is expected to stimulate innovative research and development activities pertaining to PIGL.