2 citations · 2 across the 4 of their papers we have counts for
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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-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…
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…