# overview
Wei (Wayne) Chen is an Assistant Professor in the J. Mike Walker '66 Department of Mechanical Engineering at Texas A&M University. Before his current appointment, he was a Research Scientist at Siemens Technology (Princeton, NJ) and a Postdoctoral Scholar at Northwestern University.

His research focuses on developing advanced AI and machine learning methods for engineering design, targeting some of today's most challenging problems in design ideation, design automation, and design for manufacturing. These challenges include inverse design, design under uncertainty, and the discovery of novel solutions within large design spaces. He studies how AI can augment human design capabilities by improving efficiency, stimulating creativity, and generating new knowledge and insights, while ensuring generalizability and trustworthiness in AI-driven design processes. His work has been applied to various engineering domains including aerodynamic design, materials design, design for advanced manufacturing, and CAD/CAE.

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Machine Learning And Artificial Intelligence

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Machine Learning And Artificial Intelligence

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4607 Graphics, Augmented Reality And Games

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Machine Learning And Artificial Intelligence

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4607 Graphics, Augmented Reality And Games

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4012 Fluid Mechanics And Thermal Engineering

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Networking And Information Technology R&d (nitrd)

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Machine Learning And Artificial Intelligence

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Networking And Information Technology R&d (nitrd)

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Machine Learning And Artificial Intelligence

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4007 Control Engineering, Mechatronics And Robotics

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University of Maryland, College Park - (College Park, Maryland, United States)

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Chongqing University - (Chongqing, China)

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Active Expansion Sampling for Learning Feasible Domains in an Unbounded   Input Space

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Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

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American Institute of Aeronautics and Astronautics (AIAA)

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Deep Generative Model for Efficient 3D Airfoil Parameterization and   Generation

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Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design

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Uncertainty-aware mixed-variable machine learning for materials design.

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Deep Generative Models for Geometric Design Under Uncertainty

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Adaptive Expansion Bayesian Optimization for Unbounded Global   Optimization

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Synthesizing Designs with Inter-Part Dependencies using Hierarchical Generative Adversarial Networks

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Airfoil Design Parameterization and Optimization using Bzier Generative Adversarial Networks

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

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Generative Inverse Design of Metamaterials with Functional Responses by   Interpretable Learning

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Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

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IH-GAN: A Conditional Generative Model for Implicit Surface-Based   Inverse Design of Cellular Structures

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

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Design Manifolds Capture the Intrinsic Complexity and Dimension of Design Spaces

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How Designs Differ: Non-Linear Embeddings Illuminate Intrinsic Design Complexity

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Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks

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Airfoil Design Parameterization and Optimization Using Bzier Generative Adversarial Networks

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American Institute of Aeronautics and Astronautics (AIAA)

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MO-PaDGAN: Generating Diverse Designs with Multivariate Performance   Enhancement

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t-METASET: Task-Aware Acquisition of Metamaterial Datasets Through Diversity-Based Active Learning

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BzierGAN: Automatic Generation of Smooth Curves from Interpretable   Low-Dimensional Parameters

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GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form   Geometric Uncertainty

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PaDGAN: A Generative Adversarial Network for Performance Augmented   Diverse Designs

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GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

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IH-GAN: A conditional generative model for implicit surface-based inverse design of cellular structures

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PaDGAN: Learning to Generate High-Quality Novel Designs

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Hybrid-heat effects on electrical-current aided friction stir welding of steel, and Al and Mg alloys

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ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials   Data

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MO-PaDGAN: Reparameterizing Engineering Designs for Augmented   Multi-objective Optimization

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Active expansion sampling for learning feasible domains in an unbounded input space

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ET-AL: Entropy-targeted active learning for bias mitigation in materials data

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PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs

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Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

# websites[].label
Design Innovation & Generative InTelligence (DIGIT) Lab