Digital Twin research not only focuses on creating virtual replicas of physical systems, but also enabling the bi-directionald interaction between the physical and the virtual counterpart. By integrating data from sensors, IoT devices, and advanced analytics, Digital Twins provide a dynamic, data-driven approach to understanding and optimizing complex systems. This stream of research focuses 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, uncertainty quantification, and real-time data, I aim to develop innovative solutions that bridge the gap between physical and digital domains, positioning Digital Twin as an trustworthy and intelligent learning systems that offer robust, real-time decision supports.
Journal Articles
[Karkaria, V., Lee, D., Chen, Y.-P., Yu, Y. & Chen, W., "An Attention-Based Spatio-Temporal Neural Operator for Evolving Physics. " IOP Machine Learning: Science and Technology, Nov. 2025; 6 045036.](https://iopscience.iop.org/article/10.1088/2632-2153/ae1277)
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
