7 papers
Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems
Jaewan Park, Kazuma Kobayashi, Qibang Liu +3
`Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown…
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…
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…
Sequential Neural Operator Transformer for High-Fidelity Surrogates of Time-Dependent Non-linear Partial Differential Equations
Qibang Liu, Seid Koric
Partial differential equations (PDEs) are fundamental to modeling complex and nonlinear physical phenomena, but their numerical solution often requires significant computational re…
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,…
Effects of Prompt Length on Domain-specific Tasks for Large Language Models
Qibang Liu, Wenzhe Wang, Jeffrey Willard
In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question…