40 citations · 42 across the 4 of their papers we have counts for
7 papers
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…
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…
Composing Modeling and Simulation with Machine Learning in Julia
Chris Rackauckas, Ranjan Anantharaman, Alan Edelman +10
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build acce…
Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks
Ranjan Anantharaman, Yingbo Ma, Shashi Gowda +4
Modern design, control, and optimization often requires simulation of highly nonlinear models, leading to prohibitive computational costs. These costs can be amortized by evaluatin…
Conceptual design study for heat exhaust management in the ARC fusion pilot plant
A. Q. Kuang, N. M. Cao, A. J. Creely +13
The ARC pilot plant conceptual design study has been extended beyond its initial scope [B. N. Sorbom et al., FED 100 (2015) 378] to explore options for managing ~525 MW of fusion p…