mlsanityData to 7 Oct 2026

LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures

arXiv 2402.01045 ↗First posted 1 Feb 2024cs.LG cs.CEcited by 2

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Abstract (arXiv)

This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced dynamics of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining high accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 1 Feb 2024.

Builds on

21 of 64 references.

References · 21

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9 more
  1. 2022Predicting Physics in Mesh-reduced Space with Temporal Attentioncited by 82
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  9. 2022Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecastingcited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Heterogeneous Graph Representation of Stiffened Panels with Non-Uniform Boundary Conditions and Loadscited by 2
  2. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0