I do computational design for Digital Twins that learn, decide, and adapt in real time.
I develop AI-enabled methods for real-time decision-making and design optimization under uncertainty — integrating physics-based models, AI, and design methods to engineering and optimize intelligent systems.
A dad, a husband, a Christian, and a researcher in design optimization and Digital Twins.
Originally from Kaohsiung, Taiwan, I'm a postdoc in Mechanical Engineering at Northwestern University, advised by Dr. Wei Chen in the Integrated DEsign Automation Laboratory (IDEAL). My work integrates physics-based knowledge, machine learning surrogates and uncertainty quantification to enable efficient, reliable, and online-adaptable decision-making for Digital Twin systems. I will join the Department of Mechanical Engineering at National Taiwan University as a tenure-track assistant professor starting from February 1st, 2027.
My research applies data-driven methods to the core challenges of Digital Twins and design optimization under uncertainty — integrating machine learning with physics-based models as a surrogate for complex physics, and developing algorithms for real-time, online-adaptable, intelligent decision-making. It spans four connected streams.
We develop AI-enabled Digital Twin methods that connect physical systems with dynamic digital models for real-time monitoring, prediction, control, and optimization. By integrating sensor data, machine learning, uncertainty quantification, model predictive control, reinforcement learning, and large language models, this thrust aims to support trustworthy decision-making across the full system lifecycle.
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We develop active learning and sequential decision-making algorithms that use model uncertainty to identify the most informative simulations, experiments, or data sources. This thrust aims to maximize sampling efficiency for surrogate modeling, design optimization, Digital Twins, and autonomous discovery in complex engineering systems.
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We develop data-driven surrogate models for complex engineering systems where high-fidelity simulations are accurate but too expensive for design exploration, optimization, and real-time decision-making. This thrust advances methods such as Gaussian processes, operator learning, and graph-based learning to emulate spatio-temporal physics, quantify uncertainty, improve interpretability, and bridge simulation-to-real gaps.
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We develop LLM- and agentic-AI-based frameworks that automate engineering design workflows by mining critical information from literature, patents, material databases, and experimental records. This thrust aims to support autonomous modeling, optimization, validation, and documentation for complex system design, with applications in hybrid manufacturing, product and mechanism design, materials discovery, and systems engineering.
Read more →Yi-Ping Chen in bold. Click any paper for the abstract, figure, and links. Full list on Google Scholar.
Conference talks & presentations are listed in the Talks section below.
Peer-reviewed full papers presented at conferences. Click any paper for details. Yi-Ping Chen in bold.
My full CV is embedded below. You can also download it or open it in a new tab.
I'm on the academic job market and always glad to talk research, collaboration, or mentoring. Reach out any time.