Employing statistical model emulation as a surrogate for CFD. (October 2015)

• Record Type: Journal Article
• Authors: Moonen, P.; Allegrini, J.
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 77
• Page End: 91
• Publication Date: 2015-10
• Subjects: Bayesian statistics -- Gaussian process -- Uncertainty quantification -- Emulation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.06.007
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This work focuses on substituting a computationally expensive simulator by a cheap emulator to enable studying applications where running the simulator is prohibitively expensive. The procedure consists of two steps. In a first step, the emulator is calibrated to closely mimic the simulator response for a number of pre-defined cases. In a second step the calibrated emulator is used as surrogate for the simulator in the otherwise prohibitively expensive application. An appealing feature of the proposed framework contrary to other approaches is that the uncertainty on the emulator prediction can be determined. While the proposed framework is applicable in virtually all areas of natural sciences, we discuss the approach and evaluate its performance based on a typical example in the realm of computational wind engineering, namely the determination of the wind field in an urban area. Graphical abstract: Highlights: Statistical model emulation is forwarded as alternative to physics-based simulation. Main advantages are 10 6 –10 8 times lower computational cost & controllable accuracy. Extensive performance assessment demonstrates reliability of the method. Step-by-step instructions and references are given to start using model emulation. Sensitivity to all user-based choices is studied and rules-of-thumb are suggested.