2 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Meta-Learning of Neural State-Space Models Using Data From Similar Systems
Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman
Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected…
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach
Ankush Chakrabarty
Bayesian optimization (BO) has demonstrated potential for optimizing control performance in data-limited settings, especially for systems with unknown dynamics or unmodeled perform…
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
Wenjie Xu, Colin N Jones, Bratislav Svetozarevic +2
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for improving cl…
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
Ankush Chakrabarty, Gordon Wichern, Christopher Laughman
Physics-informed dynamical system models form critical components of digital twins of the built environment. These digital twins enable the design of energy-efficient infrastructur…