Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909060934139904 |
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| author | Pourkamali-Anaraki, Farhad Husseini, Jamal F. Pineda, Evan J. Bednarcyk, Brett A. Stapleton, Scott E. |
| author_facet | Pourkamali-Anaraki, Farhad Husseini, Jamal F. Pineda, Evan J. Bednarcyk, Brett A. Stapleton, Scott E. |
| contents | This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage of the proposed framework, a machine learning model termed the "learner" identifies a limited set of candidates within the input design space whose predicted outputs closely align with desired outcomes. Subsequently, in the second stage, a separate surrogate model, functioning as an "evaluator," is employed to assess the reduced candidate space generated in the first stage. This evaluation process eliminates inaccurate and uncertain solutions, guided by a user-defined coverage level. The framework's distinctive contribution is the integration of conformal inference, providing a versatile and efficient approach that can be widely applicable. To demonstrate the effectiveness of the proposed framework compared to conventional single-stage inverse problems, we conduct several benchmark tests and investigate an engineering application focused on the micromechanical modeling of fiber-reinforced composites. The results affirm the superiority of our proposed framework, as it consistently produces more reliable solutions. Therefore, the introduced framework offers a unique perspective on fostering interactions between machine learning-based surrogate models in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation Pourkamali-Anaraki, Farhad Husseini, Jamal F. Pineda, Evan J. Bednarcyk, Brett A. Stapleton, Scott E. Machine Learning Computational Engineering, Finance, and Science This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage of the proposed framework, a machine learning model termed the "learner" identifies a limited set of candidates within the input design space whose predicted outputs closely align with desired outcomes. Subsequently, in the second stage, a separate surrogate model, functioning as an "evaluator," is employed to assess the reduced candidate space generated in the first stage. This evaluation process eliminates inaccurate and uncertain solutions, guided by a user-defined coverage level. The framework's distinctive contribution is the integration of conformal inference, providing a versatile and efficient approach that can be widely applicable. To demonstrate the effectiveness of the proposed framework compared to conventional single-stage inverse problems, we conduct several benchmark tests and investigate an engineering application focused on the micromechanical modeling of fiber-reinforced composites. The results affirm the superiority of our proposed framework, as it consistently produces more reliable solutions. Therefore, the introduced framework offers a unique perspective on fostering interactions between machine learning-based surrogate models in real-world applications. |
| title | Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2401.02008 |