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  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.
Inference in a partial differential equations model of pulmonary arterial and venous blood circulation using statistical emulation
CIBB 2016, 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 1-3 September 2016, Stirling, UK / Also in LNCS, Vol.10477
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in CIBB 2016, 13th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 1-3 September 2016, Stirling, UK / Also in LNCS, Vol.10477 and is available at : http://doi.org/10.1007/978-3-319-67834-4_15
PERMALINK : https://www.eurecom.fr/publication/5354