Transfer learning for the calibration of the inertial confinement fusion simulations

Transfer learning refers to exploiting the knowledge gained from solving one problem and applying it to solve a different but related problem. A well-known example is reusing publicly available neural network models that have been trained on large sets of images, and partially retraining them to solve a new task, for which fewer data are available. Transfer learning has not been extensively applied in physical sciences yet. In this presentation, we discuss numerical tests that have been carried out to investigate the applicability of transfer learning to calibrate the Inertial Confinement Fusion (ICF) computer simulations against the experimental data, which will be obtained at the National Ignition Facility (NIF). A neural network is initially trained to predict the simulation results and then retrained to match the sparse experimental data. A validation data set is then used to investigate the calibration accuracy as a function of the experimental data volume, retrained model capacity, and the size of discrepancy between simulations and experiments. Preliminary results are encouraging and motivate further investigation of transfer-learning-based calibration using larger data volumes.