Physics-Informed Graph Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 66 references that are in the map. First posted on arXiv 22 Feb 2022.
Builds on
33 of 66 references.
References · 33
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
- 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
- 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 329
- 2017A Survey on Network Embeddingcited by 142
- 2021Graph Learning: A Surveycited by 140
- 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
- 2019Scaling description of generalization with number of parameters in deep learningcited by 83
- 2020Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profilescited by 59
- 2021Graph Learning based Recommender Systems: A Reviewcited by 59
21 more
- 2020Learning continuous-time PDEs from sparse data with graph neural networkscited by 53
- 2020DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolationcited by 45
- 2021Dynamic Network Embedding Surveycited by 44
- 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 39
- 2020Random Walks: A Review of Algorithms and Applicationscited by 38
- 2019Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modelingcited by 26
- 2019Differentiable Physics-informed Graph Networkscited by 25
- 2021PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgerycited by 22
- 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
- 2020Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Miningcited by 20
- 2021Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsitycited by 20
- 2021Physics-informed Spline Learning for Nonlinear Dynamics Discoverycited by 18
- 2018Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approachcited by 14
- 2022CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphscited by 14
- 2020Multivariate Relations Aggregation Learning in Social Networkscited by 10
- 2020Data-driven Computational Social Science: A Surveycited by 8
- 2020Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learningcited by 7
- 2021A survey of machine learning-based physics event generationcited by 6
- 2021Network Representation Learning: From Traditional Feature Learning to Deep Learningcited by 3
- 2021Graph Force Learningcited by 3
- 2016A Distributed Multilevel Force-directed Algorithmcited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.