Redefining engineering design with trustworthy AI
Dr. Wayne Chen is advancing trustworthy AI that can generate novel, feasible engineering designs and transform how engineers discover, create and innovate.
Artificial intelligence is transforming engineering, but Dr. Wayne Chen is working to push AI beyond a supporting role and into a primary driver of design and discovery. In the mechanical engineering department at Texas A&M University, Chen’s research focuses on developing AI systems that can generate novel, feasible and trustworthy engineering solutions.
“It’s the gap between augmentation and genuine creation that inspires my long-term vision,” Chen said. “Engineering design spaces are vast and constrained by physics, manufacturability and sustainability, making them difficult to navigate with intuition alone.”
Chen’s work enables AI to propose new designs, uncover hidden relationships in data and accelerate early-stage engineering workflows. As AI takes on time-intensive tasks such as ideation, prototyping and simulation, engineers shift toward defining objectives, evaluating solutions and guiding decision-making. This leads to faster innovation and fewer late-stage failures.
A central focus of Chen’s research is trust. He emphasizes model transparency and uncertainty quantification to help engineers understand why AI proposes certain solutions and where those solutions may fail. These capabilities are essential for applying AI in high-stakes engineering applications.
One example of Chen’s work uses deep generative models to quantify geometric uncertainties in manufactured structures. By capturing complex manufacturing variations with limited data, his methods allow engineers to design structures that remain robust despite variability in real-world production.
Chen also applies AI to design ideation and generative design. In one project, a multi-agent AI system builds a knowledge base of biological structures and functions extracted from the scientific literature, enabling the discovery of non-obvious design inspirations for new materials.
Sustainability is another key component of Chen’s research. Through interdisciplinary collaborations, he integrates lifecycle considerations into AI-driven design. One project with Dr. Minghui Zheng focuses on redesigning electric vehicle battery connectors to enable automated disassembly and recycling, supporting a more circular economy.
Chen is the principal investigator on a $479,982 National Science Foundation-funded project developing interpretable, uncertainty-aware AI models for 2D material-based biosensors. The work aims to accelerate biosensor design for emerging threats while using transparent models to reveal new design insights and study how interpretability affects designer trust. He collaborates with Dr. Wei Gao in mechanical engineering and Dr. Chenglin Wu from the civil and environmental engineering department.
Chen believes the mechanical engineering department is well positioned to lead AI-driven design efforts nationally, leveraging its scale, interdisciplinary strengths and industry partnerships. Students play a central role in this vision, with graduate researchers leading projects and developing skills in AI, domain knowledge and communication.
Through his research, Chen aims to help establish a new paradigm in which AI becomes a trustworthy engine of engineering discovery, advancing materials, structures and machines while expanding fundamental understanding.
