Publications

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
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
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
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
Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods
This paper shows how to use conditional generative models in two-dimensional (2D) airfoil optimization to probabilistically predict …
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Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods
RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks
Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., …
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RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks
MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization
Multi-objective optimization is key to solving many Engineering Design problems, where design parameters are optimized for several …
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MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design …
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PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation
In aerodynamic shape optimization, the convergence and computational cost are greatly affected by the representation capacity and …
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Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation
PaDGAN: Learning to Generate High-Quality Novel Designs
Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in …
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PaDGAN: Learning to Generate High-Quality Novel Designs
Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because …
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Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks
Real-world designs usually consist of parts with inter-part dependencies, i.e., the geometry of one part is dependent on one or …
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Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body …
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Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
Beyond the Known: Detecting Novel Feasible Domains over an Unbounded Design Space
To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit …
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Beyond the Known: Detecting Novel Feasible Domains over an Unbounded Design Space
Design Manifolds Capture the Intrinsic Complexity and Dimension of Design Spaces
This paper shows how to measure the intrinsic complexity and dimensionality of a design space. It assumes that high-dimensional design …
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Design Manifolds Capture the Intrinsic Complexity and Dimension of Design Spaces