2 citations · 2 across the 4 of their papers we have counts for
6 papers
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…
ARCO-BO: Adaptive Resource-aware COllaborative Bayesian Optimization for Heterogeneous Multi-Agent Design
Zihan Wang, Yi-Ping Chen, Tuba Dolar +1
Modern scientific and engineering design increasingly involves distributed optimization, where agents such as laboratories, simulations, or industrial partners pursue related goals…
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-…
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…