Batch Sequential Minimum Energy Design with Design-Region Adaptation

Abstract

Experiments related to nanofabrication often face challenges of resource-limited experimental budgets, highly demanding tolerance requirements, and complicated response surfaces. Therefore, wisely selecting design points is crucial in order to minimize the expense of resources while at the same time ensuring that enough information is gained to accurately address the experimental goals. In this paper, an efficient batch-sequential design methodology is proposed for optimizing high-cost, low-resource experiments with complicated response surfaces. Through the sequential learning of the unknown response surface, the proposed method sequentially narrows down the design space to more important subregions and selects a batch of design points in the reduced design region. The proposed method balances the space filling of the design region and the search for the optimal operating condition. The performance of the proposed method is demonstrated on a nanowire synthesis system as well as on an optimization test function.

• Batch Sequential Design
• Combined Design Criteria
• Experiments
• Optimization
• Space-Filling Design

Additional information

Notes on contributors

Heeyoung Kim

Dr. Kim is an Assistant Professor in the Department of Industrial and Systems Engineering, KAIST. Her email address is [email protected].

Justin T. Vastola

Dr. Vastola is an Operations Research Consultant at Revenue Analytics, Inc. His email address is [email protected].

Sungil Kim

Dr. Kim is an Assistant Professor in the School of Management Engineering, UNIST. His email address is [email protected].

Jye-Chyi Lu

Dr. Lu is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. His email address is [email protected].

Martha A. Grover

Dr. Grover is a Professor in the School of Chemical & Biomolecular Engineering, Georgia Institute of Technology. Her email address is [email protected].