Understanding Scientific Processes via Sequential History Matching and Emulation of Computer Models

Samuel Jackson (University of Southampton)

Friday 30th April, 2021 15:00-16:00 https://www.smartsurvey.co.uk/s/MNW32H/


Computer models are essential for aiding the understanding of real-world processes of interest. History matching aims to find the set of all possible combinations of computer model input rate parameters which are not inconsistent with observed data, gathered from a collection of physical experiments, given all the sources of uncertainty involved with the model and the measurements. Analysis of this set permits understanding of the links between the model, the parameter space and experimental observations, thus allowing the model to be informative for the corresponding scientific process.

Additional insight can be gained from sequential history matching - namely analysing how the sets of acceptable parameters changes in accordance with successive sets of physical observations. We discuss how sequential history matching, often making use of statistical emulators (approximations) of the model, can be informative for many scientific analyses. In addition, this methodology naturally extends to aiding choice of the most appropriate physical experiment for answering specific scientific questions of interest. We demonstrate our techniques on an important systems biology model of hormonal crosstalk in the roots of an Arabidopsis plant.

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