2024/01/04 by Farhad Pourkamali‐Anaraki, Pourkamali-Anaraki, Farhad, Jamal F. Husseini +7
Computer Science · Decision Sciences · Engineering · #Advanced Multi-Objective Optimization Algorithms #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Probabilistic and Robust Engineering Design #Topology Optimization in Engineering #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2401.02008
openalex publication_date 2024/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.