Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-10-17

AUTHORS

Umberto Noè , Weiwei Chen , Maurizio Filippone , Nicholas Hill , Dirk Husmeier

ABSTRACT

The present article addresses the problem of inference in a multiscale computational model of pulmonary arterial and venous blood circulation. The model is a computationally expensive simulator which, given specific parameter values, solves a system of nonlinear partial differential equations and returns predicted pressure and flow values at different locations in the arterial and venous blood vessels. The standard approach in parameter calibration for computer code is to emulate the simulator using a Gaussian Process prior. In the present work, we take a different approach and emulate the objective function itself, i.e. the residual sum of squares between the simulations and the observed data. The Efficient Global Optimization (EGO) algorithm [2] is used to minimize the residual sum of squares. A generalization of the EGO algorithm that can handle hidden constraints is described. We demonstrate that this modified emulator achieves a reduction in the computational costs of inference by two orders of magnitude. More... »

PAGES

184-198

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67834-4_15

DOI

http://dx.doi.org/10.1007/978-3-319-67834-4_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092236331


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