基于预训练变压器的约束工程问题快速准确的贝叶斯优化。

IF 3.6 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed
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引用次数: 0

摘要

贝叶斯优化是工程设计优化中一种有效处理约束条件多、评估代价高的黑盒函数的基本策略。本文介绍了一种基于先验数据拟合网络(PFNs)的基础变压器模型贝叶斯优化约束处理框架。与传统方法不同,每个约束需要单独的高斯过程(GP)模型,我们的框架利用PFN的变压器架构,使用上下文学习在单个向前传递中同时评估目标和约束。通过对跨越合成、结构和工程设计挑战的15个测试问题的综合基准测试,我们证明了与传统的基于gp的受限预期改进(CEI)方法相比,在保持或提高解决方案质量的同时,其速度提高了一个数量级。我们的方法通过快速找到可行的、最优的解决方案,特别擅长解决工程问题。这个评估工程设计中新的BO算法的基准框架将在https://github.com/rosenyu304/BOEngineeringBenchmark上发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast and accurate Bayesian optimization with pre-trained transformers for constrained engineering problems.

Bayesian Optimization (BO) is a foundational strategy in engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a novel constraint-handling framework for Bayesian Optimization (BO) using Prior-data Fitted Networks (PFNs), a foundation transformer model. Unlike traditional approaches requiring separate Gaussian Process (GP) models for each constraint, our framework leverages PFN's transformer architecture to evaluate objectives and constraints simultaneously in a single forward pass using in-context learning. Through comprehensive benchmarking across 15 test problems spanning synthetic, structural, and engineering design challenges, we demonstrate an order of magnitude speedup while maintaining or improving solution quality compared to conventional GP-based methods with constrained expected improvement (CEI). Our approach particularly excels at engineering problems by rapidly finding feasible, optimal solutions. This benchmark framework for evaluating new BO algorithms in engineering design will be published at https://github.com/rosenyu304/BOEngineeringBenchmark.

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来源期刊
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization 工程技术-工程:综合
CiteScore
7.60
自引率
15.40%
发文量
304
审稿时长
3.6 months
期刊介绍: The journal’s scope ranges from mathematical foundations of the field to algorithm and software development, and from benchmark examples to case studies of practical applications in structural, aero-space, mechanical, civil, chemical, naval and bio-engineering. Fields such as computer-aided design and manufacturing, uncertainty quantification, artificial intelligence, system identification and modeling, inverse processes, computer simulation, bio-mechanics, bio-medical applications, nano-technology, MEMS, optics, chemical processes, computational biology, meta-modeling, DOE and active control of structures are covered when the topic is closely related to the optimization of structures or fluids. Structural and Multidisciplinary Optimization publishes original research papers, review articles, industrial applications, brief notes, educational articles, book reviews, conference diary, forum section, discussions on papers, authors´ replies, obituaries, announcements and society news.
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