{"title":"Two-stage surrogate modeling for data-driven design optimization with application to composite microstructure generation","authors":"","doi":"10.1016/j.engappai.2024.109436","DOIUrl":null,"url":null,"abstract":"<div><div>Design optimization or inverse problems, which involve determining input parameters to achieve specific desired outputs, are essential in many engineering applications. Conventional artificial intelligence methods for solving inverse problems rely on single-stage surrogate modeling. However, these methods can be limited in their ability to fully represent the complex relationship between inputs and outputs, hindering a comprehensive exploration of potential solutions and overlooking valid alternatives. To address this challenge, this paper presents a novel two-stage framework that combines two distinct machine learning models: a “learner” and an “evaluator”. The learner identifies a subset of candidate inputs whose predicted outputs closely match the target. The evaluator then refines this selection by further narrowing the input space, resulting in more precise predictions. A key innovation is the incorporation of conformal inference, a statistical technique that quantifies prediction uncertainty in a distribution-free setting and is applicable to any machine learning model. The framework’s effectiveness is validated through extensive benchmark testing using a simulated data set and an engineering case study on artificial microstructure generation of fiber-reinforced composites. The results demonstrate that this two-stage approach significantly outperforms traditional single-stage methods, consistently delivering more accurate and reliable solutions. For instance, the framework consistently identifies input configurations that closely align with two desired descriptors across varying fiber counts, while single surrogate models yield solutions far from the targets without any precautions. This paper is concluded by discussing potential future enhancements, including the integration of deep learning models and strategies for addressing distribution shifts within the framework.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095219762401594X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Design optimization or inverse problems, which involve determining input parameters to achieve specific desired outputs, are essential in many engineering applications. Conventional artificial intelligence methods for solving inverse problems rely on single-stage surrogate modeling. However, these methods can be limited in their ability to fully represent the complex relationship between inputs and outputs, hindering a comprehensive exploration of potential solutions and overlooking valid alternatives. To address this challenge, this paper presents a novel two-stage framework that combines two distinct machine learning models: a “learner” and an “evaluator”. The learner identifies a subset of candidate inputs whose predicted outputs closely match the target. The evaluator then refines this selection by further narrowing the input space, resulting in more precise predictions. A key innovation is the incorporation of conformal inference, a statistical technique that quantifies prediction uncertainty in a distribution-free setting and is applicable to any machine learning model. The framework’s effectiveness is validated through extensive benchmark testing using a simulated data set and an engineering case study on artificial microstructure generation of fiber-reinforced composites. The results demonstrate that this two-stage approach significantly outperforms traditional single-stage methods, consistently delivering more accurate and reliable solutions. For instance, the framework consistently identifies input configurations that closely align with two desired descriptors across varying fiber counts, while single surrogate models yield solutions far from the targets without any precautions. This paper is concluded by discussing potential future enhancements, including the integration of deep learning models and strategies for addressing distribution shifts within the framework.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.