Original paper
Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion
Abstract
We adopt a technique, known in the machine learning community as transfer learning, to reduce the bias of computer simulation using very sparse experimental data. Unlike the Bayesian calibration, which is commonly used to estimate the simulation bias, the transfer learning approach discussed in this article involves calculating an artificial neural network surrogate model of the simulations. Assuming that the simulation code correctly predicts...
Paper Details
Title
Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion
Published Date
Jan 1, 2020
Volume
48
Issue
1
Pages
46 - 53
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