113 citations · 179 across the 11 of their papers we have counts for
12 papers
Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
Hanfeng Zhai, Hongtao Qiao, Hassan Mansour +1
We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary diff…
Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
Young Jin Park, Francois Germain, Jing Liu +6
Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building,…
Meta-Learning for Physically-Constrained Neural System Identification
Ankush Chakrabarty, Gordon Wichern, Vedang M. Deshpande +3
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorpor…
Violation-Aware Contextual Bayesian Optimization for Controller 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 to be effective for improv…
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…
ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling
Yingbo Ma, Shashi Gowda, Ranjan Anantharaman +3
Getting good performance out of numerical equation solvers requires that the user has provided stable and efficient functions representing their model. However, users should not be…
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…
Optimization of circuitry arrangements for heat exchangers using derivative-free optimization
Nikolaos Ploskas, Christopher Laughman, Arvind U. Raghunathan +1
Optimization of the refrigerant circuitry can improve a heat exchanger's performance. Design engineers currently choose the refrigerant circuitry according to their experience and…