Autors: Bataleblu, A.,Bakhtiari, Z., Roshanian, J., Ginchev, D. G.
Title: Reinforcement Learning Applied to Multidisciplinary Systems Design Optimization of an Aerial Vehicle
Keywords: multidisciplinary, systems design, optimization

Abstract: The engineering design problems have been highly complex and time-consuming. Real-world engineering systems also suffer many systems and subsystems levels challenges during the entire product life cycle because of their inevitable multidisciplinary nature and complex coupling between different subsystems. Therefore, any effort in direction of alleviating difficulties in the field of Multidisciplinary Systems Design Optimization (MSDO) will receive remarkable attention. Artificial Intelligence (AI) which is a massive field encompassing many goals has recently triggered a paradigm shift in numerous industries around the world and also could be led to a revolution in the MSDO field of research. Currently, the most influential topic in AI is machine learning (ML) which is decomposed into supervised learning, unsupervised learning, semi-supervised learning, and Reinforcement Learning (RL). The focus of this article is to show the further applications of RL in the field of MSDO research are

References

    Issue

    Bultrans 2022, 2022, Bulgaria,

    Цитирания (Citation/s):
    1. Benyamin Ebrahimi, Ali Asghar Bataleblu, Jafar Roshanian, Developing an intelligent systems design framework based on multidisciplinary design analysis and multi-agent thinking integration February 2024 Expert Systems with Applications 248(6) DOI: 10.1016/j.eswa.2024.123363 - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в национален форум с межд. уч., публикация в реферирано издание, индексирана в Scopus