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20202026
most citedDeep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

367 citations · 378 across the 6 of their papers we have counts for

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Showing cs.CEShow all

6 papers · 1 filter

cs.CE2026

One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automata

Yujie Xiang, Liwei Wang

Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable…

cs.CE20251 cited

Data-Driven Topology Optimization for Multiscale Biomimetic Spinodal Design

Shiguang Deng, Doksoo Lee, Aaditya Chandrasekhar +4

Spinodoid architected materials have drawn significant attention due to their unique nature in stochasticity, aperiodicity, and bi-continuity. Compared to classic periodic truss-,…

cs.CE2023

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Doksoo Lee, Wei Wayne Chen, Liwei Wang +2

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that a…

cs.CE20214 cited

Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization

Liwei Wang, Anton van Beek, Daicong Da +3

For natural frequency optimization of engineering structures, cellular composites have been shown to possess an edge over solid. However, existing multiscale design methods for cel…

cs.CE2020367 cited

Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

Liwei Wang, Yu-Chin Chan, Faez Ahmed +3

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inve…

cs.CE20205 cited

Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process

Liwei Wang, Siyu Tao, Ping Zhu +1

The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly…