How can machine learning support the practice of modeling and simulation? —A review and directions for future research

Elbattah, Mahmoud

Abstract

The use of Machine Learning (ML) has achieved a significant momentum across a very wide range of domains. This paper aims to provide a meeting point for discussing the integration of Modeling and Simulation (M&S) with ML. The discussion presents arguments in favour of why and how the M&S practice can avail of ML in different modalities. In this context, the paper reviews key studies published over the past 6 years in main venues including Winter Simulation Conference, SIGSIM PADS, and DS-RT. Further aspects are discussed, which could help reinforce the utilisation of ML in the M&S arena. In general, the study is conceived to foster the presentation of potential ideas and speculative directions towards availing of data-driven knowledge provided by ML.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)
• Start Date: Oct 7, 2019
• End Date: Oct 9, 2019
• Online Publication Date: Jan 16, 2020
• Publication Date: Jan 16, 2020
• Deposit Date: Nov 22, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 1-7
• Book Title: 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)
• ISBN: 9781728129242
• DOI: https://doi.org/10.1109/ds-rt47707.2019.8958703
• Public URL: https://uwe-repository.worktribe.com/output/13461824