Information-driven optimal experimental design with deep neural network surrogate model for composite materials

Abstract

The optimal experimental design (OED) provides informative experimental resources and reduces the inherent uncertainty of the experiment. The OED is a framework to maximize performed experiments by controlling design variables. This paper proposes the OED framework combined with deep neural network (DNN) surrogate model to find the optimal design over large and complex design space. The OED has the optimization process to maximize the conditional mutual information (CMI) between hidden properties and composite structural performances. Approximate coordinate exchange (ACE) algorithm was applied to find the optimal design over a high and complicated design space. For fast optimization, surrogate DNN model was used instead of finite element analysis. Two examples were executed for the demonstration of the OED frameworks. From the results of each demonstration, OED provided the results that are consistent with heuristic knowledge.

Keywords:

• Optimal experimental design
• composite failure
• conditional mutual information
• deep neural networks
• surrogate modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Korea Evaluation Institute of Industrial Technology (KEIT) grant of the Korea government [No.20013598, Industrial Technology Innovation Program] and Institute of Engineering Research at Seoul National University. Authors are grateful for their supports.