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.
Laser Scan Path Design for Controlled Microstructure in Additive Manufacturing with Integrated Reduced-Order Phase-Field Modeling and Deep Reinforcement Learning
Computational Science Program, The University of Texas at El Paso（德克萨斯理工大学计算科学计划）；Department of Aerospace and Mechanical Engineering, The University of Texas at El Paso（德克萨斯理工大学航空航天与机械工程系）；School of Manufacturing Systems and Networks, Arizona State University（亚利桑那州立大学制造系统与网络学院）
Predicting Stress and Damage in Carbon Fiber-Reinforced Composites Deformation Process using Composite U-Net Surrogate Model
Department of Chemical and Biomolecular Engineering, University of Notre Dame（化学与生物分子工程系，圣母大学）；Department of Aerospace Engineering, University of Colorado Boulder（航空航天工程系，科罗拉多大学波德分校）；Department of Aerospace and Mechanical Engineering, University of Notre Dame（航空航天与机械工程系，圣母大学）；Air Force Research Laboratory, Wright-Patterson Air Force Base（空军研究实验室，威格斯-帕特森空军基地）
On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks
Graduate Student, Department of Aerospace Engineering, Auburn University, Auburn, AL（航空航天工程系研究生，阿伯丁大学，阿伯丁，AL）；Sr. R&D Staff Member, Manufacturing Science Division, ORNL, Knoxville, TN（制造科学部高级研发人员，ORNL，克里夫兰，TN）；Research Aerospace Engineer, Multiscale and Multiphysics Modeling Branch, NASA Glenn Research Center, Cleveland, OH（多尺度和多物理场建模分支研究航空航天工程师，NASA格伦研究中心，克里夫兰，OH）；Professor, Department of Aerospace Engineering, Auburn University, Auburn, AL（航空航天工程系教授，阿伯丁大学，阿伯丁，AL）；Assistant Professor, Department of Aerospace Engineering, Auburn University, Auburn, AL（航空航天工程系助理教授，阿伯丁大学，阿伯丁，AL）
Crack-Net: Prediction of Crack Propagation in Composites
College of Engineering, Peking University（北京大学工学院）；Imperial College London（帝国理工学院）；Eastern Institute for Advanced Study（宁波东方理工大学（暂名） 东方高等研究院）；Peng Cheng Laboratory（鹏城实验室）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Meituan（美团）
Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Graz University of Technology（格拉茨工业大学）；Graz Center for Computational Engineering（格拉茨计算工程中心）；University Medicine Essen（埃森大学医学院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）
