Smart Sampling Algorithm for Surrogate Model Development

•Novel sampling technique for surrogate modelling of arbitrary functions.•Sample points are placed adaptively based on spatial and quality considerations.•Optimal sample placement is formulated as an NLP using surrogate models.•Our technique outperforms uniform, random, and Sobol sampling on 1-varia...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 96; pp. 103 - 114
Main Authors: Garud, Sushant Suhas, Karimi, I.A., Kraft, Markus
Format: Journal Article
Language:English
Published: Elsevier Ltd 04.01.2017
Subjects:
Accuracy
Adaptive surrogate modelling
Algorithms
Chemical engineering
Computational efficiency
Mathematical analysis
Mathematical models
Nonlinear programming
Point placement
Sampling
Smart sampling
Smart sampling
Point placement
Adaptive surrogate modelling
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary:•Novel sampling technique for surrogate modelling of arbitrary functions.•Sample points are placed adaptively based on spatial and quality considerations.•Optimal sample placement is formulated as an NLP using surrogate models.•Our technique outperforms uniform, random, and Sobol sampling on 1-variable test problems. Surrogate modelling aims to reduce computational costs by avoiding the solution of rigorous models for complex physicochemical systems. However, it requires extensive sampling to attain acceptable accuracy over the entire domain. The well-known space-filling techniques use sampling based on uniform, quasi-random, or stochastic distributions, and are typically non-adaptive. We present a novel technique to select sample points systematically in an adaptive and optimized manner, assuring that the points are placed in regions of complex behaviour and poor representation. Our proposed smart sampling algorithm (SSA) solves a series of surrogate-based nonlinear programming problems for point placement to enhance the overall accuracy and reduce computational burden. Our extensive numerical evaluations using 1-variable test problems suggest that our SSA performs the best, when its initial sample points are generated using uniform sampling. For now, this conclusion is valid for 1-variable functions only, and we are testing our algorithm for n-variable functions.
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ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2016.10.006