Digital Twin research focuses on creating virtual replicas of physical systems, processes, or objects that enable real-time simulation, monitoring, and analysis. By integrating data from sensors, IoT devices, and advanced analytics, digital twins provide a dynamic, data-driven approach to understanding and optimizing complex systems. My research centers on leveraging Digital Twin technology for system design, performance optimization, and lifecycle management, with an emphasis on enhancing decision-making and predictive capabilities. Through the integration of AI, machine learning, and real-time data, I aim to develop innovative solutions that bridge the gap between physical and digital domains, driving efficiency and innovation in engineering and beyond.
Journal Articles
Chen, Y. P., Tsai, Y.-K., Karkaria, V., & Chen, W., "Uncertainty-Aware Digital Twins: Robust Model Predictive Control Using Time-Series Deep Quantile Learning. " ASME Journal of Mechanical Design, Special Issue: Data Driven Design under Uncertainty, February 2026; 148(2): 021702.
Chen, Y.-P., Karkaria, V., Tsai, Y.-K., Rolark, F., Quispe, D., Gao, R. X., Cao, J., & Chen, W., “Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks” Journal of Manufacturing Systems, 80(2025): 412-424.
Karkaria, V., Tsai, Y. K., Chen, Y. P., & Chen, W., “An Optimization-Centric Review on Integrating Artificial Intelligence and Digital Twin Technologies in Manufacturing”, Engineering Optimization, 1-47 (2025).
Conference Talk
Chen, Y.-P., Tsai, Y.-K., Karkaria, V., Chen, W., “Uncertainty-Aware Digital Twin: a Simultaneous Multistep Robust Model Predictive Control for Additive Manufacturing”, United State National Congress on Computational Mechanics (USNCCM) 18, Chicago, IL, USA
Chen, Y.-P., Karkaria, V., Tsai, Y.-K., Rolark, F., Quispe, D., Gao, R. X., Cao, J., & Chen, W., “Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks” North American Manufacturing Research Conference 53, Greenville, SC, USA
