Scalable algorithms for physics-informed neural and graph networks

Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George E. Karniadakis

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations


Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

Original languageEnglish (US)
Article numbere24
JournalData-Centric Engineering
Issue number6
StatePublished - Jun 29 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Engineering
  • Computer Science Applications
  • Applied Mathematics


  • Graph calculus
  • PINNs
  • graph neural networks
  • scalability
  • scientific machine learning


Dive into the research topics of 'Scalable algorithms for physics-informed neural and graph networks'. Together they form a unique fingerprint.

Cite this