Wei (Wayne) Chen

Principal investigator
CV
weichen_cv.pdf
GitHub
wchen459
Google Scholar
Wei (Wayne) Chen
Email
w.chenobfuscate@tamu.edu

Dr. Wei (Wayne) Chen is an Assistant Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University. He received his Ph.D. in Mechanical Engineering from the University of Maryland, College Park in 2019. Prior to his current appointment, he was a Research Scientist at Siemens Technology (Princeton, NJ) and a Postdoctoral Scholar at Northwestern University. His research focuses on how artificial intelligence and machine learning can assist humans in solving challenging design problems including high-dimensional problems, inverse design, design under uncertainty, and novel design synthesis. His work has been applied to various engineering domains including aerodynamic design, materials design, design for manufacturing, and CAD/CAE.

Papers

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

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

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

Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design

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

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

RANGE-GAN: Design Synthesis Under Constraints Using Conditional Generative Adversarial Networks

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

PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation

PaDGAN: Learning to Generate High-Quality Novel Designs

Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks

Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

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

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

Design Manifolds Capture the Intrinsic Complexity and Dimension of Design Spaces