3 papers
cs.CE2026
Physics-guided diffusion models for inverse design of disordered metamaterials
Ziyuan Xie, Weipeng Xu, Dazhi Zhao +6
Disordered metamaterials are promising for programming physical properties across diverse applications, yet their inverse design remains challenging due to the non-intuitive struct…
cs.LG2024
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer +3
Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessit…
eess.SP2024
Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing
Ning Liu, Xuxiao Li, Manoj R. Rajanna +6
A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate, behaves as a virtual twin of the re…