Article-Journal

Inverse Design of Nonlinear Mechanics of Bio-inspired Materials Through Interface Engineering and Bayesian Optimization
In many biological materials such as nacre and bone, the material structure consists of hard grains and soft interfaces, with the …
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Inverse Design of Nonlinear Mechanics of Bio-inspired Materials Through Interface Engineering and Bayesian Optimization
The Future of Digital Twin Research and Development
While digital twin (DT) has made significant strides in recent years, much work remains to be done in the research community and in the …
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The Future of Digital Twin Research and Development
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 …
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Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
In this work, we propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training …
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Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are …
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Data-Driven Design for Metamaterials and Multiscale Systems: A Review
ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are …
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ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching …
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Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly …
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GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a …
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t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, …
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IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures