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February 16, 2024Open Access

Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes

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TWTobias WürthKarlsruhe Institute of TechnologyNFNiklas FreymuthKarlsruhe Institute of TechnologyCZClemens ZimmerlingKarlsruhe Institute of Technology

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Würth et al. (2024) studied this question.

synapsesocial.com/papers/68e78f4db6db6435877009b0https://doi.org/10.48550/arxiv.2402.10681
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs2024
  2. 2M‐PINN : A mesh‐based physics‐informed neural network for linear elastic problems in solid mechanics2024 · 110 citations
  3. 3PINN-MG: A physics-informed neural network for mesh generation2025
  4. 4A fully differentiable GNN-based PDE Solver: With Applications to Poisson and Navier-Stokes Equations2024
  5. 5Benchmarking Physics-Informed Neural Networks for Gravitational Potential Modeling2026