.png){kind=icon}
返回
A sparse multi-fidelity surrogate-based optimization method with computational awareness
.png){kind=icon}
PRE
AI
DOI：10.1007/s00366-022-01766-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
CoKriging is a popular surrogate modeling approach to approximate the input-output relationship using multi-fidelity data from different sources. However, it suffers from the big data issue due to its cubic time complexity and square memory complexity with the data size. This becomes even exacerbated for iterative design optimization. To overcome this limitation, this paper presents a new data sparsification method for multi-fidelity surrogate-based optimization (MFSBO). It includes two key components: reduced design space and data filtering (RDS & DF), which alleviate the surrogate modeling complexity and time for improved efficiency while balancing between exploration and exploitation during optimization. RDS & DF is also combined with an expected improvement reduction (EIR)-based infill technique (Yang et al. in Struct Multidiscip Optim, 2022. https://doi.org/10.1007/s00158-022-03240-x), enabling both parsimony and computational awareness for MFSBO. Two case studies are conducted to examine the proposed method. Results demonstrate that a significant reduction in the modeling and thus optimization time (69.49% and 73.92%) is achieved while retaining the design accuracy.
Keyword:
Big data
Gaussian process
CoKriging
Reduced design space
Microfluidic concentration gradient generator
期刊
IF:
4.9
论文数:
2.6K
被引数:
9.3K
机构
引用论文
A radial microfluidic concentration gradient generator with high-density channels for cell apoptosis assay.png)
LAB ON A CHIP
LAB ON A CHIPIF5.4
PREAI
分享收藏Serotherapy of B-cell neoplasms with anti-B4-blocked ricin: a phase I trial of daily bolus infusionBlood
OAAI
BloodIF0
OAAI分享收藏
查看更多内容