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-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.
- Access the article at the publisher: DOI: 10.1016/j.compchemeng.2016.10.006
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