ERGO: A New Robust Design Optimization Technique Combining Multi-Objective Bayesian Optimization With Analytical Uncertainty Quantification

稳健性(进化) 计算机科学 多目标优化 不确定度量化 数学优化 贝叶斯概率 贝叶斯优化 替代模型 稳健优化 人工智能 最优化问题 机器学习 数学 生物化学 基因 化学
作者
Jolan Wauters
出处
期刊:Journal of Mechanical Design 卷期号:144 (3) 被引量:10
标识
DOI:10.1115/1.4052009
摘要

Abstract In this work, robust design optimization (RDO) is treated, motivated by the increasing desire to account for variability in the design phase. The problem is formulated in a multi-objective setting with the objective of simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective. To account for the computational cost that is often encountered in RDO problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted optimization-under-uncertainty schemes. The Gaussian processes, the surrogates on which Bayesian optimization builds, are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of these surrogates is known, an analytical treatment of the problem is available. To obtain the quantities of interest without sampling an analytical uncertainty, propagation through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement, obtaining the novel efficient robust global optimization scheme. The method is tested on a series of test cases to examine its behavior for varying difficulties and validated on an aerodynamic test function which proves the effectiveness of the novel scheme.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大气惜天完成签到 ,获得积分10
2秒前
现代皓轩关注了科研通微信公众号
2秒前
wqidoctor完成签到,获得积分10
3秒前
3秒前
3秒前
4秒前
frankk发布了新的文献求助10
4秒前
5秒前
5秒前
5秒前
文献自由完成签到 ,获得积分10
6秒前
6秒前
科研通AI5应助舒芙蕾采纳,获得10
6秒前
科目三应助LEE123采纳,获得10
6秒前
阿霍完成签到,获得积分20
7秒前
SYLH应助雨碎寒江采纳,获得10
7秒前
7秒前
尹不愁完成签到,获得积分10
7秒前
wanci应助Elva采纳,获得10
8秒前
frankk完成签到,获得积分10
8秒前
8秒前
8R60d8应助五星大厨小熊采纳,获得10
8秒前
9秒前
9秒前
幽默不愁发布了新的文献求助10
9秒前
看文章的小余完成签到,获得积分10
9秒前
科研通AI5应助柔弱丝袜采纳,获得10
9秒前
白华苍松发布了新的文献求助10
10秒前
HIMINNN发布了新的文献求助20
10秒前
HarryQ完成签到,获得积分10
11秒前
11秒前
自由的秋灵完成签到,获得积分10
11秒前
李健应助过儿采纳,获得10
11秒前
毛毛发布了新的文献求助10
12秒前
12秒前
hahaha完成签到,获得积分10
12秒前
xin应助郭躺平采纳,获得20
13秒前
13秒前
热情十三完成签到 ,获得积分10
14秒前
ly发布了新的文献求助10
15秒前
高分求助中
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Les Mantodea de Guyane Insecta, Polyneoptera 1000
工业结晶技术 880
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3490736
求助须知:如何正确求助?哪些是违规求助? 3077538
关于积分的说明 9149233
捐赠科研通 2769733
什么是DOI,文献DOI怎么找? 1519934
邀请新用户注册赠送积分活动 704390
科研通“疑难数据库(出版商)”最低求助积分说明 702148