3 papers
cs.LG2025
Geometry-Informed Neural Operator Transformer
Qibang Liu, Weiheng Zhong, Hadi Meidani +3
Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiri…
cs.CE2025
A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Weiheng Zhong, Qibang Liu, Diab Abueidda +2
Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and…
physics.comp-ph2025
Towards Signed Distance Function based Metamaterial Design: Neural Operator Transformer for Forward Prediction and Diffusion Model for Inverse Design
Qibang Liu, Seid Koric, Diab Abueidda +2
The inverse design of metamaterial architectures presents a significant challenge, particularly for nonlinear mechanical properties involving large deformations, buckling, contact,…