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• W4386602600 abstract "Classification boundaries play a crucial role in various chemical engineering applications; however, existing methods of finding them are often inefficient and require a substantial number of experiments. Here, three sequential sampling methods are introduced to improve the accuracy and efficiency of finding classification boundaries with binary data outputs as compared with grid-based sampling methods. Each of the three methods is applied to three distinct scenarios: a simple analytical shower problem, finding a bulk oxidation boundary, and finding a two-phase liquid–gas region. Using our methods, we are able to consistently identify classification boundaries and quantify the boundary uncertainty at a higher accuracy and with fewer experiments compared with grid sampling methods. We achieve this with active-learning-inspired techniques by increasing sampling densities in regions with higher relevance to experimental goals. This is possible through interpretable model informed predictions." @default.
• W4386602600 created "2023-09-12" @default.
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• W4386602600 date "2023-09-11" @default.
• W4386602600 modified "2023-10-10" @default.
• W4386602600 title "Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications" @default.
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• W4386602600 doi "https://doi.org/10.1021/acs.iecr.3c02362" @default.
• W4386602600 hasPublicationYear "2023" @default.
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