View in EDS

MeshODENet: A Graph-Informed Neural Ordinary Differential Equation Neural Network for Simulating Mesh-Based Physical Systems.

Saved in:
Bibliographic Details
Title: MeshODENet: A Graph-Informed Neural Ordinary Differential Equation Neural Network for Simulating Mesh-Based Physical Systems.
Authors: Liu, Kangzheng1 kangzhen@asu.edu, Ma, Leixin1 leixin.ma@asu.edu
Source: Journal of Applied Mechanics. May2026, Vol. 93 Issue 5, p1-10. 10p.
Subjects: Graph neural networks, Continuous time models, Elastic deformation, Prediction models, Computer simulation, Structural mechanics
Abstract: The simulation of complex physical systems using a discretized mesh is a cornerstone of applied mechanics, but traditional numerical solvers are often computationally prohibitive for many-query tasks. While graph neural networks (GNNs) have emerged as powerful surrogate models for mesh-based data, their standard autoregressive application for long-term prediction is often plagued by error accumulation and instability. To address this, we introduce MeshODENet, a general framework that synergizes the spatial reasoning of GNNs with the continuous-time modeling of neural ordinary differential equations. We demonstrate the framework's effectiveness and versatility on a series of challenging structural mechanics problems, including different elastic bodies undergoing large, nonlinear deformations. The results demonstrate that our approach significantly outperforms baseline models in long-term predictive accuracy and stability, while achieving substantial computational speed-ups over traditional solvers. This work presents a powerful and generalizable approach for developing data-driven surrogates to accelerate the analysis and modeling of complex structural systems. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Mechanics is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 193518147
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: MeshODENet: A Graph-Informed Neural Ordinary Differential Equation Neural Network for Simulating Mesh-Based Physical Systems.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Kangzheng%22">Liu, Kangzheng</searchLink><relatesTo>1</relatesTo><i> kangzhen@asu.edu</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Leixin%22">Ma, Leixin</searchLink><relatesTo>1</relatesTo><i> leixin.ma@asu.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Mechanics%22">Journal of Applied Mechanics</searchLink>. May2026, Vol. 93 Issue 5, p1-10. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+time+models%22">Continuous time models</searchLink><br /><searchLink fieldCode="DE" term="%22Elastic+deformation%22">Elastic deformation</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+mechanics%22">Structural mechanics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The simulation of complex physical systems using a discretized mesh is a cornerstone of applied mechanics, but traditional numerical solvers are often computationally prohibitive for many-query tasks. While graph neural networks (GNNs) have emerged as powerful surrogate models for mesh-based data, their standard autoregressive application for long-term prediction is often plagued by error accumulation and instability. To address this, we introduce MeshODENet, a general framework that synergizes the spatial reasoning of GNNs with the continuous-time modeling of neural ordinary differential equations. We demonstrate the framework's effectiveness and versatility on a series of challenging structural mechanics problems, including different elastic bodies undergoing large, nonlinear deformations. The results demonstrate that our approach significantly outperforms baseline models in long-term predictive accuracy and stability, while achieving substantial computational speed-ups over traditional solvers. This work presents a powerful and generalizable approach for developing data-driven surrogates to accelerate the analysis and modeling of complex structural systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Applied Mechanics is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193518147
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1115/1.4071488
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Graph neural networks
        Type: general
      – SubjectFull: Continuous time models
        Type: general
      – SubjectFull: Elastic deformation
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Structural mechanics
        Type: general
    Titles:
      – TitleFull: MeshODENet: A Graph-Informed Neural Ordinary Differential Equation Neural Network for Simulating Mesh-Based Physical Systems.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Liu, Kangzheng
      – PersonEntity:
          Name:
            NameFull: Ma, Leixin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 00218936
          Numbering:
            – Type: volume
              Value: 93
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Journal of Applied Mechanics
              Type: main
ResultId 1