.png){kind=icon}
返回
Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures
.png){kind=icon}
OA
AI
DOI：10.1016/j.finel.2020.103400.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the main issues when dealing with the numerical optimization of mechanical structures is the balance between computation time and model accuracy. The work presented herein aims at accelerating global optimization by using the framework of Bayesian optimization on a quantity of interest together with multiple levels of fidelity. These multi-fidelity data are generated from a model-order reduction framework: the LATIN Proper Generalized Decomposition. Within this framework, a reduced-order basis is generated on-the-fly and re-exploited to reduce the computational cost of observations. This strategy is illustrated on two elasto-viscoplastic test-cases for which significant speedups can be observed.
Keyword:
Bayesian optimization
Multi-fidelity kriging
Reduced-order models
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
2.6K
被引数:
5.1K
机构
引用论文
.png){kind=icon}
Final-State Interaction in the L3 X-ray Absorption Spectra of Mixed-Valence Ce and Yb Compounds
PREAI
分享收藏
分享收藏
查看更多内容