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. Preprints2024, 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
Hjulstad, J.; Hovd, M. Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels. Preprints2024, 2024050241. https://doi.org/10.20944/preprints202405.0241.v1
APA Style
Hjulstad, J., & Hovd, M. (2024). Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels. Preprints. https://doi.org/10.20944/preprints202405.0241.v1
Chicago/Turabian Style
Hjulstad, J. and Morten Hovd. 2024 "Indirect Model Predictive Control on Epidemiological Stochastic Blockmodels" Preprints. https://doi.org/10.20944/preprints202405.0241.v1
Abstract
This paper aims to demonstrate how the detectionof communities for epidemiological networks can be used toreduce the dimensionality of optimal control problems. A rangeof planted partition stochastic blockmodels are generated, wherethe underlying communities in the model vary in detectability. Ahigh-performance computing workflow is then used to simulateand estimate reduced model dynamics for the networks, whichin 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.
