Inference in a partial differential equations model of pulmonary arterial and venous blood circulation using statistical emulation

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics - 13th International Meeting, CIBB 2016, Revised Selected Papers
EditorsGiulio Caravagna, Roberto Tagliaferri, David Gilbert, Andrea Bracciali
PublisherSpringer Verlag
Pages184-198
Number of pages15
ISBN (Print)9783319678337
DOIs
StatePublished - 2017
Event13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2016 - Stirling, United Kingdom
Duration: Sep 1 2016Sep 3 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10477 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2016
Country/TerritoryUnited Kingdom
CityStirling
Period09/1/1609/3/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

  • Efficient global optimization
  • Emulation
  • Gaussian processes
  • Global optimization
  • Hidden constraints
  • Nonlinear differential equations
  • Pulmonary blood circulation
  • Pulmonary hypertension
  • Simulator
  • Statistical inference

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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