Adaptive surrogate modeling for high-dimensional spatio-temporal output

This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is computationally expensive and consists of a large number of inputs and outputs. Surrogate models are often constructed t...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2022-10, Vol.65 (10), Article 300
Main Authors: Kapusuzoglu, Berkcan, Mahadevan, Sankaran, Matsumoto, Shunsaku, Miyagi, Yoshitomo, Watanabe, Daigo
Format: Article
Language:English
Subjects:
Adaptive sampling
Computational Mathematics and Numerical Analysis
Computer programming
Dimensional analysis
Engineering
Engineering Design
Errors
Gas turbine engines
Model accuracy
Modelling
Optimization
Physics
Research Paper
Theoretical and Applied Mechanics
Thermomechanical analysis
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Summary:This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of spatio-temporal multi-physics systems is computationally expensive and consists of a large number of inputs and outputs. Surrogate models are often constructed to replace the physics-based model to achieve computational efficiency in analyses such as uncertainty quantification and optimization that require many function calls. In order to address the challenge introduced by the high dimensionality of spatio-temporal output, a dimension reduction method is first employed to map the high-dimensional output to a low-dimensional latent space. This is followed by the construction of the surrogate model in the low-dimensional space. The prediction error in the original space, which includes both the reconstruction error and surrogate model error, is evaluated using different error metrics. Based on the prediction accuracy of the surrogate model, new training points are identified for adaptive improvement of the surrogate model. We present a novel adaptive sampling technique that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model. Thermo-mechanical analysis of a gas turbine engine blade is used to analyze the effectiveness of the proposed method.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03402-x