Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
This provides an incremental improvement for researchers and engineers dealing with data-driven design optimization, particularly in materials science.
The paper tackles inverse problems in scientific and engineering design by proposing a two-stage surrogate modeling framework that first identifies candidate solutions and then evaluates them using conformal inference, demonstrating its effectiveness in composite microstructure generation with more reliable solutions compared to conventional methods.
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.