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Authors: Zong-De Jian 1 ; Hung-Jui Chang 2 ; Tsan-sheng Hsu 1 and Da-Wei Wang 1

Affiliations: 1 Academia Sinica, Taiwan ; 2 Academia Sinica and National Taiwan University, Taiwan

ISBN: 978-989-758-265-3

Keyword(s): Deep Learning, Surrogate, Disease Simulator.

Related Ontology Subjects/Areas/Topics: Agent Based Modeling and Simulation ; Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Complex Systems Modeling and Simulation ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Sensor Networks ; Simulation and Modeling ; Simulation Tools and Platforms ; Software and Architectures ; Symbolic Systems

Abstract: The deep learning approach has been applied to many domains with success. We use deep learning to construct the surrogate function to speed up simulation based optimization in epidemiology. The simulator is an agent-based stochastic model for influenza and the optimization problem is to find vaccination strategy to minimize the number of infected cases. The optimizer is a genetic algorithm and the fitness function is the simulation program. The simulation is the bottleneck of the optimization process. An attempt to use the surrogate function with table lookup and interpolation was reported before. The preliminary results show that the surrogate constructed by deep learning approach outperforms the interpolation based one, as long as similar cases of the testing set have been available in the training set. The average of the absolute value of relative error is less than 0.7 percent, which is quite close to the intrinsic limitation of the stochastic variation of the simulation software 0.2 percent, and the rank coefficients are all above 0.99 for cases we studied. The vaccination strategy recommended is still to vaccine the school age children first which is consistent with the previous studies. The preliminary results are encouraging and it should be a worthy effort to use machine learning approach to explore the vast parameter space of simulation models in epidemiology. (More)

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Paper citation in several formats:
Jian Z., Chang H., Hsu T. and Wang D. (2017). Learning from Simulated World - Surrogates Construction with Deep Neural Network.In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-265-3, pages 83-92. DOI: 10.5220/0006418100830092

@conference{simultech17,
author={Zong-De Jian and Hung-Jui Chang and Tsan-sheng Hsu and Da-Wei Wang},
title={Learning from Simulated World - Surrogates Construction with Deep Neural Network},
booktitle={Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2017},
pages={83-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006418100830092},
isbn={978-989-758-265-3},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Learning from Simulated World - Surrogates Construction with Deep Neural Network
SN - 978-989-758-265-3
AU - Jian Z.
AU - Chang H.
AU - Hsu T.
AU - Wang D.
PY - 2017
SP - 83
EP - 92
DO - 10.5220/0006418100830092

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