Abstract
The problem with computer model calibration by tuning the parameters associated with computer models is significant in many engineering and scientific applications. Although several methods have been established to estimate the calibration parameters, research focusing on the design of calibration parameters remains limited. Therefore, this paper proposes a sequential computer experiment design based on the D-optimal criterion, which can efficiently tune the calibration parameters while improving the prediction ability of the calibrated computer model. Numerical comparisons of the simulated and real data demonstrate the efficiency of the proposed technique.
Original language | English |
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Article number | 1375 |
Journal | Mathematics |
Volume | 10 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 May 2022 |
Keywords
- calibration
- computer models
- fisher information
- sequential D-optimal
- surrogate model