Article
Version 1
Preserved in Portico This version is not peer-reviewed
Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels
Version 1
: Received: 2 May 2024 / Approved: 6 May 2024 / Online: 6 May 2024 (07:49:54 CEST)
How to cite: Hjulstad, J.; Hovd, M. Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels. Preprints 2024, 2024050241. https://doi.org/10.20944/preprints202405.0241.v1 Hjulstad, J.; Hovd, M. Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels. Preprints 2024, 2024050241. https://doi.org/10.20944/preprints202405.0241.v1
Abstract
This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.
Keywords
identification for control; optimization and control of large-scale network systems; Monte Carlo methods; high performance computing
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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