Machine learning algorithms in ship design optimization

被引：0
|
作者：
Peri, Daniele [1 ]
机构：
[1] CNR IAC Natl Res Council, Ist Applicazioni Calcolo Mauro Picone, Via Taurini 19, I-00185 Rome, Italy
来源：
SHIP TECHNOLOGY RESEARCH | 2024年 / 71卷 / 01期
关键词：
Design optimization; global optimization; machine learning; APPROXIMATION; METAMODELS;
D O I：
10.1080/09377255.2023.2250160
中图分类号：
U6 [水路运输]; P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
Numerical optimization of complex systems benefits from the technological development of computing platforms in the last 20 years. Unfortunately, this is still not enough, and a large computational time is necessary for the solution of optimization problems when mathematical models that implement rich (and therefore realistic) physical models are adopted. In this paper, we show how the combination of optimization and Artificial Intelligence (AI), in particular Machine Learning algorithms, can help, strongly reducing the overall computational times, making also possible the use of complex simulation systems within the optimization cycle. Original approaches are proposed.
引用
收藏
页码：1 / 13
页数：13
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