96 citations · 119 across the 9 of their papers we have counts for
10 papers
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria +3
Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift. Ma…
Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learning
Ying-Kuan Tsai, Yi-Ping Chen, Vispi Karkaria +1
Active suspension systems are critical for enhancing vehicle comfort, safety, and stability, yet their performance is often limited by fixed hardware designs and control strategies…
Adaptive Digital Twin of Sheet Metal Forming via Proper Orthogonal Decomposition-Based Koopman Operator with Model Predictive Control
Yi-Ping Chen, Derick Suarez, Ying-Kuan Tsai +6
Digital Twin (DT) technologies are transforming manufacturing by enabling real-time prediction, monitoring, and control of complex processes. Yet, applying DT to deformation-based…
Digital Twin-enabled Multi-generation Control Co-Design with Deep Reinforcement Learning
Ying-Kuan Tsai, Vispi Karkaria, Yi-Ping Chen +1
Control Co-Design (CCD) integrates physical and control system design to improve the performance of dynamic and autonomous systems. Despite advances in uncertainty-aware CCD method…
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics
Vispi Karkaria, Doksoo Lee, Yi-Ping Chen +2
In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-…
Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning
Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria +1
Digital Twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control s…
Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks
Yi-Ping Chen, Vispi Karkaria, Ying-Kuan Tsai +5
Digital Twin -- a virtual replica of a physical system enabling real-time monitoring, model updating, prediction, and decision-making -- combined with recent advances in machine le…
Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
Alexander D. Zemskov, Yao Fu, Runchao Li +9
In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucia…
A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring
Vispi Karkaria, Jie Chen, Christopher Luey +4
We introduce a novel digital twin framework for predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the digital twin f…
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Vispi Karkaria, Anthony Goeckner, Rujing Zha +6
Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material incons…