.png){kind=icon}
返回
Implementation of a Bayesian Optimization Framework for Interconnected Systems
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.iecr.4c02915.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Bayesian optimization (BO) is an effective paradigm for the optimization of expensive-to-sample systems. Standard BO learns the performance of a system f(x) by using a Gaussian Process (GP) model; this treats the system as a black box and limits its ability to exploit available structural knowledge (e.g., physics and sparse interconnections in a complex system). Gray-box modeling, wherein the performance function is treated as a composition of known and unknown intermediate functions f(x, y(x)) (where y(x) is a GP model), offers a solution to this limitation; however, generating an analytical probability density for f from the Gaussian density of y(x) is often an intractable problem (e.g., when f is nonlinear). Previous work has handled this issue by using sampling techniques or by solving an auxiliary problem over an augmented space where the values of y(x) are constrained by confidence intervals derived from the GP models; such solutions are computationally intensive. In this work, we provide a detailed implementation of a recently proposed gray-box BO paradigm, BOIS, that uses adaptive linearizations of f to obtain analytical expressions for the statistical moments of the composite function. We show that the BOIS approach enables the exploitation of structural knowledge, such as that arising in interconnected systems as well as systems that embed multiple GP models and combinations of physics and GP models. We benchmark the effectiveness of BOIS against standard BO and existing gray-box BO algorithms using a pair of case studies focused on chemical process optimization and design. Our results indicate that BOIS performs as well as or better than existing gray-box methods, while also being less computationally intensive.
Keyword:
DESIGN
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
机构
引用论文
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledgeGryffin: 一种基于专家知识的分类变量贝叶斯优化算法.png)
APPLIED PHYSICS REVIEWS
OAAI
APPLIED PHYSICS REVIEWSIF11.6
OAAI分享收藏Taking the Human Out of the Loop: A Review of Bayesian Optimization将人类带出循环: 贝叶斯优化的回顾PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Simulation Failure-Robust Bayesian Optimization for Data-Driven Parameter Estimation数据驱动参数估计的仿真故障鲁棒贝叶斯优化
PREAI
分享收藏
查看更多内容