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RISReconstruction and Generation of Porous Metamaterial Units Via Variational Graph Autoencoder and Large Language Model
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid ...
Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. This complete connectivity is crucial for manufacturability and structure-fluid ...
Designing Mixed-Category Stochastic Microstructures by Deep Generative Model-Based and Curvature Functional-Based Methods
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Bridging the gaps among various categories of stochastic microstructures remains a challenge in the design representation of microstructural materials. Each microstructure category requires certain unique mathematical and ...