6 papers
Deep material network for homogenization of piezoelectric composites
Ting-Ju Wei, Yen-Ming Lu, Chuin-Shan Chen
Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical performance can be tailored by…
A Parametric Multiscale Surrogate Framework Based on Texture-Generalizable Deep Material Networks for Polycrystal Modeling
Ting-Ju Wei, Tung-Huan Su, Chuin-Shan Chen
This work presents a computational framework for parametric multiscale surrogate modeling of polycrystalline materials. The framework integrates a physics-based Deep Material Netwo…
Deep Material Network: Overview, applications and current directions
Ting-Ju Wei, Wen-Ning Wan, Chuin-Shan Chen
The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across differ…
Efficient Nonlinear Multiscale Prediction for Unseen Polycrystalline Textures via Self-Supervised Microstructure Pretraining
Ting-Ju Wei, Chuin-Shan Chen
Predicting the nonlinear mechanical response of polycrystalline materials across diverse crystallographic textures remains computationally prohibitive, and existing reduced-order s…
Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction
Ting-Ju Wei, Chuin-Shan Chen
We present the Material Masked Autoencoder (MMAE), a self-supervised Vision Transformer pretrained on a large corpus of short-fiber composite images via masked image reconstruction…
Orientation-aware interaction-based deep material network in polycrystalline materials modeling
Ting-Ju Wei, Tung-Huan Su, Chuin-Shan Chen
Multiscale simulations are indispensable for connecting microstructural features to the macroscopic behavior of polycrystalline materials, but their high computational demands limi…