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@inbook{Kucherenko:2021:10.1016/B978-0-323-88506-5.50136-4,
author = {Kucherenko, S and Klymenko, O and Shah, N},
booktitle = {Computer Aided Chemical Engineering},
doi = {10.1016/B978-0-323-88506-5.50136-4},
pages = {875--881},
title = {Application of Machine Learning and Global Sensitivity Analysis for Identification and Visualization of Design Space},
url = {http://dx.doi.org/10.1016/B978-0-323-88506-5.50136-4},
year = {2021}
}

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TY  - CHAP
AB  - The design space (DS) is defined as the combination of materials and process conditions which provides assurance of quality for a pharmaceutical. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space (e.g. material properties). We demonstrate that application of metamodel-based filters and global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying DS. Once DS is identified it is necessary to present it graphically. The output of identification of DS is a multi-dimensional probability map. The projection of the multi-dimensional DS to a 2D representation is still unavoidable irrespectively of the method used to reach such probability mapping. We showed that application of constraint GSA can dramatically reduce the number of required for visualization 2D projections.
AU  - Kucherenko,S
AU  - Klymenko,O
AU  - Shah,N
DO  - 10.1016/B978-0-323-88506-5.50136-4
EP  - 881
PY  - 2021///
SP  - 875
TI  - Application of Machine Learning and Global Sensitivity Analysis for Identification and Visualization of Design Space
T1  - Computer Aided Chemical Engineering
UR  - http://dx.doi.org/10.1016/B978-0-323-88506-5.50136-4
ER  -

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Professor Nilay Shah OBE FREng

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