LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
Siboni, and Dierk Raabe
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.
citing papers explorer
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LEIA: Learned Environment for Interactive Architected Materials
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
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Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
Neural network surrogate uses area fraction, shape descriptor τ, two-point correlation S2(r), and lineal-path function ℓ(z) to predict effective properties of hyperelastic Boolean microstructures, with added descriptors improving pointwise accuracy but not guaranteeing physical admissibility.