RAAL: Resource Aware Active Learning for Multifidelity Efficient Optimization

被引:5
作者
Grassi, Francesco [1 ]
Manganini, Giorgio [1 ]
Garraffa, Michele [1 ]
Mainini, Laura [2 ]
机构
[1] Raytheon Technol Res Ctr, Autonomous & Intelligent Syst Dept, Cork T23 XN53, Ireland
[2] Raytheon Technol Res Ctr, Appl Res & Technol, Collins Aerosp, Cork T23 XN53, Ireland
关键词
GLOBAL OPTIMIZATION; SURROGATE; UNCERTAINTY; SELECTION; DESIGN; MODELS;
D O I
10.2514/1.J061383
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
[No abstract available]
引用
收藏
页码:2744 / 2753
页数:10
相关论文
共 48 条
[1]  
[Anonymous], 2012, Bayesian approach to global optimization: theory and applications, DOI DOI 10.1007/978-94-009-0909-0
[2]  
Beran Philip S., 2020, AIAA AVIATION 2020 F
[3]   Advances in surrogate based modeling, feasibility analysis, and optimization: A review [J].
Bhosekar, Atharv ;
Ierapetritou, Marianthi .
COMPUTERS & CHEMICAL ENGINEERING, 2018, 108 :250-267
[4]  
Brochu E., PREPRINT
[5]  
Bryson D. E., 2017, THESIS U DAYTON DAYT
[6]  
Chakraborty S., PREPRINT
[7]  
Cutajar K., PREPRINT
[8]  
Cutler M, 2014, IEEE INT CONF ROBOT, P3888, DOI 10.1109/ICRA.2014.6907423
[9]   MILP models for the selection of a small set of well-distributed points [J].
D'Ambrosio, Claudia ;
Nannicini, Giacomo ;
Sartor, Giorgio .
OPERATIONS RESEARCH LETTERS, 2017, 45 (01) :46-52
[10]  
Eldred M., 2006, 11 AIAA ISSMO MULT A, P7117, DOI [DOI 10.2514/6.2006-7117, https://doi.org/10.2514/6.2006-7117]