TY - GEN
T1 - Using Bayesian optimization with knowledge transfer for high computational cost design
T2 - ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
AU - Kaya, Mine
AU - Hajimirza, Shima
N1 - Publisher Copyright:
Copyright © 2019 ASME.
PY - 2019
Y1 - 2019
N2 - Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.
AB - Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.
KW - Bayesian optimization
KW - External quantum efficiency
KW - Thin film solar cells
KW - Transfer optimization
UR - http://www.scopus.com/inward/record.url?scp=85076492754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076492754&partnerID=8YFLogxK
U2 - 10.1115/DETC2019-98111
DO - 10.1115/DETC2019-98111
M3 - Conference contribution
AN - SCOPUS:85076492754
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 45th Design Automation Conference
Y2 - 18 August 2019 through 21 August 2019
ER -