6533b838fe1ef96bd12a5039
RESEARCH PRODUCT
Active emulation of computer codes with Gaussian processes – Application to remote sensing
Luca MartinoDaniel Heestermans SvendsenGustau Camps-vallssubject
FOS: Computer and information sciencesComputer Science - Machine LearningActive learningActive learning (machine learning)Computer sciencemedia_common.quotation_subjectMachine Learning (stat.ML)Radiative transfer model02 engineering and technology01 natural sciencesMachine Learning (cs.LG)symbols.namesakeArtificial IntelligenceStatistics - Machine Learning0103 physical sciences0202 electrical engineering electronic engineering information engineeringCode (cryptography)Emulation010306 general physicsFunction (engineering)Gaussian processGaussian process emulatorGaussian processRemote sensingmedia_commonEmulationbusiness.industrySampling (statistics)Remote sensingSignal ProcessingGlobal Positioning Systemsymbols020201 artificial intelligence & image processingComputer codeComputer Vision and Pattern RecognitionbusinessHeuristicsSoftwareDesign of experimentsdescription
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations, model tractability, as well as compact and expressive simulated datasets. In order to achieve this, the proposed Active Multi-Output Gaussian Process Emulator (AMOGAPE) combines the predictive capacity of Gaussian Processes (GPs) with the design of an acquisition function that favors sampling in low density and fluctuating regions of the approximation functions. Comparing different acquisition functions, we illustrate the promising performance of the method for the construction of emulators with toy examples, as well as for a widely used remote sensing transfer code.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 | Pattern Recognition |