LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures
arXiv:2402.01045 · doi:10.1007/s00366-024-02034-7
Abstract
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.
References in corpus (9)
- A Comprehensive Survey on Graph Neural Networks
- Geometric deep learning: going beyond Euclidean data
- DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
- Data-driven computational mechanics
- Learning data driven discretizations for partial differential equations
- Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models
- Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
- MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
- A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials