Active learning for efficiently training emulators of computationally expensive mathematical models
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An emulator is a fast-to-evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as model calibration and value-of-information analyses. Emulators are developed using the output of simulators at specific input values (design points). Developing an emulator that closely approximates the simulator can require many design points, which becomes computationally expensive. We describe a self-terminating active learning algorithm to efficiently develop emulators tailored to a specific emulation task. Its postulated advantages over the prevalent approaches include (1) self-termination and (2) development of emulators with smaller mean squared errors. To explicate, we develop and compare Gaussian Process emulators of a prostate screening model using the adaptive algorithm versus standard approaches.
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