arXiv subjects

Chuin-Shan Chen

Publications and source records attributed to Chuin-Shan Chen.

6 recordsLinked to original sources

arXiv2026

Deep material network for homogenization of piezoelectric composites

Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical performance can be tailored by combining constituent phases and microstructural architecture. However, conventional computational homogenization based on direct numerical simulation (DNS) is computationally expensive, particularly for multiscale simulations and material design tasks that require repeated homogenization analyses. To address this limitation, this work proposes a piezoelectric deep material network (PDMN) to efficiently homogenize two-phase piezoelectric composites. The proposed framework embeds the governing electromechanical homogenization relations directly into the network architecture, yielding a physics-informed, semi-analytical surrogate that explicitly captures the two-way coupling between the mechanical and electrical fields across constituent phases. The network is trained offline on linear electroelastic datasets and, through a fully coupled Newton--Raphson solution with a consistent electromechanical tangent, subsequently used for efficient online prediction under broader constitutive settings, including nonlinear electroelasticity and history-dependent responses. The framework is validated on two-phase composites of polyvinylidene fluoride (PVDF) and lithium niobate (LiNbO$_3$) with reversed phase arrangements under nonlinear electroelastic loading, and on a viscoelastic--piezoelectric composite exhibiting coupled stress relaxation. Numerical examples show that the proposed PDMN achieves high predictive accuracy while reducing the computational cost by more than three orders of magnitude compared with DNS. The proposed framework, therefore, provides an efficient and reliable surrogate for the multiscale analysis and design of piezoelectric composites.

cs.CE↗

arXiv2025

Efficient Nonlinear Multiscale Prediction for Unseen Polycrystalline Textures via Self-Supervised Microstructure Pretraining

Predicting the nonlinear mechanical response of polycrystalline materials across diverse crystallographic textures remains computationally prohibitive, and existing reduced-order surrogates are typically fit to a single microstructural realization, precluding reuse on unseen textures. We address both limitations through a self-supervised pretraining strategy. A three-dimensional masked autoencoder is pretrained on 100,000 voxelized synthetic face-centered cubic (FCC) microstructures whose textures systematically span the texture hull via hierarchical simplex sampling, yielding transferable, texture-aware latent representations. A differentiable homogenization operator then maps these representations to the parameters of an orientation-aware interaction-based deep material network (ODMN). Given a previously unseen microstructure, the pretrained encoder infers a standalone ODMN that, coupled with crystal plasticity, reproduces the nonlinear loading-unloading-reloading stress-strain response with mean relative error below 2%, at a 304x CPU-time speedup over full-field crystal-plasticity direct numerical simulation. The pretrained representation is also highly data-efficient: on a label-limited homogenized-stiffness regression task, pretraining raises the validation R^2 from below 0.1 (trained from scratch) to above 0.8. Together, these results demonstrate that self-supervised pretraining yields physically meaningful, transferable microstructural representations and provides a scalable framework for microstructure-property inference. The present scope (FCC systems with equiaxed grains) is a deliberate first step, with extensions to morphological texture and other crystal systems outlined.

cs.CE↗

arXiv2025

A Parametric Multiscale Surrogate Framework Based on Texture-Generalizable Deep Material Networks for Polycrystal Modeling

This work presents a computational framework for parametric multiscale surrogate modeling of polycrystalline materials. The framework integrates a physics-based Deep Material Network (DMN), specifically an Orientation-aware interaction-based Deep Material Network (ODMN), with two data-driven components: a Texture-Adaptive Clustering and Sampling (TACS) scheme that provides a reduced yet statistically consistent representation of crystallographic texture, and a Graph Neural Network (GNN) that infers the micromechanical equilibrium parameters of the ODMN from grain-level interaction graphs. Referred to as the TACS-GNN-ODMN framework, this combination introduces a microstructure-to-parameter mapping that constructs fully parameterized surrogate models for previously unseen microstructures without retraining. The resulting surrogate model preserves the micromechanical structure of the underlying formulation while substantially reducing computational cost. Numerical results show that the framework accurately predicts nonlinear mechanical responses and crystallographic texture evolution under several loading conditions, in close agreement with full-field direct numerical simulations (DNS). The method further achieves more than two orders of magnitude speed-up over fast Fourier transform (FFT)-based simulations. The framework thus provides an efficient and physically consistent strategy for multiscale modeling of polycrystalline materials and is well suited to large-scale simulations and real-time applications.

cs.CE↗

arXiv2025

Deep Material Network: Overview, applications and current directions

The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across different length scales. Unlike conventional data-driven approaches, the trainable parameters in DMN possess clear physical interpretations-they encode the geometric characteristics of representative volume elements (RVEs) rather than serving as purely statistical fitting parameters . By employing a hierarchical tree structure, DMN learns the homogenization behavior associated with microstructural geometry. Consequently, it can be trained exclusively on linear elastic datasets while effectively extrapolating to nonlinear responses during online prediction, making it a highly efficient and scalable approach for multiscale simulations. From a broader perspective, DMN can be viewed as a physics-informed reduced-order model that captures the essential micromechanical features governing macroscopic behavior. Its hierarchical formulation provides a compact yet interpretable representation of the RVE response, significantly reducing computational costs compared to direct numerical simulations. This review elaborates on the theoretical foundation, training methodology, and recent extensions of DMN, emphasizing its role as a unifying framework that connects data-driven learning with physically interpretable multiscale modeling.

cs.CE↗

arXiv2025

Orientation-aware interaction-based deep material network in polycrystalline materials modeling

Multiscale simulations are indispensable for connecting microstructural features to the macroscopic behavior of polycrystalline materials, but their high computational demands limit their practicality. Deep material networks (DMNs) have been proposed as efficient surrogate models, yet they fall short of capturing texture evolution. To address this limitation, we propose the orientation-aware interaction-based deep material network (ODMN), which incorporates an orientation-aware mechanism and an interaction mechanism grounded in the Hill-Mandel principle. The orientation-aware mechanism learns the crystallographic textures, while the interaction mechanism captures stress-equilibrium directions among representative volume element (RVE) subregions, offering insight into internal microstructural mechanics. Notably, ODMN requires only linear elastic data for training yet generalizes effectively to complex nonlinear and anisotropic responses. Our results show that ODMN accurately predicts both mechanical responses and texture evolution under complex plastic deformation, thus expanding the applicability of DMNs to polycrystalline materials. By balancing computational efficiency with predictive fidelity, ODMN provides a robust framework for multiscale simulations of polycrystalline materials.

cs.CE↗

arXiv2024

Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction

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. The pretrained MMAE learns latent representations that capture essential microstructural features and are broadly transferable across tasks. We demonstrate two key applications: (i) predicting homogenized stiffness components through fine-tuning on limited data, and (ii) inferring physically interpretable parameters by coupling MMAE with an interaction-based material network (IMN), thereby enabling extrapolation of nonlinear stress-strain responses. These results highlight the promise of microstructure foundation models and lay the groundwork for future extensions to more complex systems, such as 3D composites and experimental datasets.

cs.CE↗