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- W4384788302 abstract "In this work, a novel approach for the development of surrogate models is presented. It aims to combine traditional model-based design of experiments and global estimability analysis. The approach is based on the Fisher Information Matrix to set two objective functions to be maximized. The first is given by D-optimal design of experiments criterion that maximizes the determinant of FIM, thus minimizing the volume of the confidence region and resulting in more precise parameter estimate. The second is given by the sum of Euclidian norms of FIM’s columns to improve the estimability of all the unknown parameters. The resulting multi-objective optimization problem is solved to determine the Pareto front of the optimal solutions. The Multi-Attribute Utility Theory is used as a decision making aid method to select the best solution, needed for the development of the surrogate model. For demonstration purposes, a polynomial Response Surface Model (RSM) based surrogate is developed. It mimics a computationally expensive first-principles model that predicts the conversion rate of a phosphate ore digestion by phosphoric acid. The results are very promising, showing how the approach allows to select the most estimable model parameters from intelligently designed data points. High performance of the developed surrogate is demonstrated by comparing its predictions and computation time to those obtained by first-principles model." @default.
- W4384788302 created "2023-07-20" @default.
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- W4384788302 date "2023-01-01" @default.
- W4384788302 modified "2023-10-09" @default.
- W4384788302 title "Global estimability analysis and model-based design of experiments in surrogate modeling" @default.
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- W4384788302 doi "https://doi.org/10.1016/b978-0-443-15274-0.50102-5" @default.
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