arXiv subjects

Wei "Wayne" Chen

Publications and source records attributed to Wei "Wayne" Chen.

4 recordsLinked to original sources

arXiv2026

Scalable Active Metamaterials for Shape-Morphing

Shape-morphing materials allow structures such as morphing wings, soft robots, and deployable devices to change their shape on demand without mechanical joints or motors. Aperiodic active metamaterials, in which every unit is designed individually, can realize complex programmed shape changes, but their design by structural optimization requires repeated analyses of the full structure, at a cost that grows rapidly with the number of units. This paper presents a scalable active metamaterial (SAM) framework that designs such structures without simulating the assembled structure. The design domain is discretized into unit cells composed of a stiff frame with compliant hinges and a soft, thermally active infill. The stiff frame limits the interaction between neighboring unit cells, which approximately decouples the design problem into two scales. At the macroscale, the deformation required for each unit cell is obtained by a constrained Laplacian mesh-editing formulation with soft data-driven feasibility constraints. At the microscale, the infill geometry of every unit cell is generated by a conditional diffusion model trained on finite-strain finite element simulations of unit cells. All designs are verified by geometrically nonlinear finite element simulations of the assembled structures. The design cost grows approximately linearly with the number of unit cells. The framework is demonstrated on single-point actuation, multi-point control, and shape matching tasks. We further analyze the sources of the remaining error and discuss possible ways to mitigate it. This work establishes a foundation for the systematic, scalable design of highly complex shape-morphing active metamaterials and for understanding the mechanisms governing their target-matching accuracy.

cs.CE↗

arXiv2025

GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses

Inverse design is a common yet challenging engineering problem, particularly for nonlinear functional responses such as mechanical behavior or spectral analysis. Deep generative models are motivated by intractability, non-existence or non-uniqueness of solutions, and the need for rapid solution-space exploration. In this study, we show that deep generative model-based and optimization-based approaches can provide incomplete solutions or hallucinate given out-of-distribution targets. To address this, we propose the Generative and Uncertainty-informed Inverse Design (GUIDe) framework, which leverages probabilistic machine learning, statistical inference, and Markov chain Monte Carlo to generate designs with targeted nonlinear behaviors. Instead of inverse mappings, i.e., response $\mapsto$ design, GUIDe adopts design $\mapsto$ response: a forward model predicts each design's nonlinear functional response and evaluates the confidence under a user-specified tolerance. Sampling the solution space by this confidence yields diverse feasible designs. Our validation on nacre-inspired materials finds solutions beyond the training range, even under out-of-distribution targets.

cs.CE↗

arXiv2025

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data

This paper introduces GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning), a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials. GUST leverages the representational power of deep generative models to learn a high-dimensional conditional distribution of as-fabricated unit cell geometries given nominal designs, thereby enabling uncertainty quantification. To address the scarcity of real-world manufacturing data, GUST employs a two-stage learning process. First, it leverages self-supervised pretraining on a large-scale synthetic dataset to capture the structure variability inherent in metamaterial geometries and an approximated distribution of as-fabricated geometries given nominal designs. Subsequently, GUST employs transfer learning by fine-tuning the pretrained model on limited real-world manufacturing data, allowing it to adapt to specific manufacturing processes and nominal designs. With only 960 unit cells additively manufactured in only two passes, GUST can capture the variability in geometry and effective material properties. In contrast, directly training a generative model on the same amount of real-world data proves insufficient, as demonstrated through both qualitative and quantitative comparisons. This scalable and cost-effective approach significantly reduces data requirements while maintaining the effectiveness in learning complex, real-world geometric uncertainties, offering an affordable method for free-form geometric uncertainty quantification in the manufacturing of metamaterials. The capabilities of GUST hold significant promise for high-precision industries such as aerospace and biomedical engineering, where understanding and mitigating manufacturing uncertainties are critical.

cs.LG↗

arXiv2024

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metamaterials with functional responses can exhibit varying properties under different conditions (e.g., wave-based responses or deformation-induced property variation). This work addresses the rapid inverse design of such metamaterials to meet target qualitative functional behaviors, a challenge due to its intractability and non-unique solutions. Unlike data-intensive and non-interpretable deep-learning-based methods, we propose the Random-forest-based Interpretable Generative Inverse Design (RIGID), a single-shot inverse design method for fast generation of metamaterial designs with on-demand functional behaviors. RIGID leverages the interpretability of a random forest-based "design$\rightarrow$response" forward model, eliminating the need for a more complex "response$\rightarrow$design" inverse model. Based on the likelihood of target satisfaction derived from the trained random forest, one can sample a desired number of design solutions using Markov chain Monte Carlo methods. We validate RIGID on acoustic and optical metamaterial design problems, each with fewer than 250 training samples. Compared to the genetic algorithm-based design generation approach, RIGID generates satisfactory solutions that cover a broader range of the design space, allowing for better consideration of additional figures of merit beyond target satisfaction. This work offers a new perspective on solving on-demand inverse design problems, showcasing the potential for incorporating interpretable machine learning into generative design under small data constraints.

physics.optics↗