.png){kind=icon}
返回
Learning surrogate models for simulation-based optimization
.png){kind=icon}
OA
AI
DOI：10.1002/aic.14418.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments is addressed. A methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible is proposed. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique that leverages an integer programming formulation to allow for the efficient consideration of a large number of possible functional components in the model. The model is then improved systematically through the use of derivative-free optimization solvers to adaptively sample new simulation or experimental points. Automated learning of algebraic models for optimization (ALAMO), the computational implementation of the proposed methodology, along with examples and extensive computational comparisons between ALAMO and a variety of machine learning techniques, including Latin hypercube sampling, simple least-squares regression, and the lasso is described. (c) 2014 American Institute of Chemical Engineers AIChE J, 60: 2211-2227, 2014
Keyword:
design (process simulation)
optimization
machine learning
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:
4
论文数:
1.1W
被引数:
2.9W
机构
引用论文
.png){kind=icon}
l-serine dehydratase and l-serine-pyruvate aminotransferase activities in different animal species不同动物物种的l-丝氨酸脱水酶和l-丝氨酸-丙酮酸转氨酶活性
PREAI
分享收藏分享收藏Metamodels for computer-based engineering design: survey and recommendations基于计算机的工程设计的元模型: 调查和建议
PREAI
分享收藏
查看更多内容