Jupyter notebooks for Gaussian Process

This data repository contains Jupyter notebooks used to generate and analyze "Taming nuclear mass models with Gaussian processes" reported in the associated NST publication.File description:plot_RBF_2D-for-DB.ipynb: this jupyter notebook post-processes Gaussian Process regression using a two-dimensional input space and a Radial Basis Function (RBF) kernel.plot_MAT32_2D-for-DB.ipynb: this jupyter notebook post-processes Gaussian Process regression using a two-dimensional input space and a Matérn 3/2 kernel.plot_gp_MAT_various_input-for-DB.ipynb: this jupyter notebook post-processes Gaussian Process regression using an 8D input space and a Matérn 3/2 kernel.plot_theory_theory_8D-for-DB.ipynb: this jupyter notebook performs theory-to-theory validation using FRDM12 as “pseudo-experimental” data.Data description:2D_prediction_RBF_fix_noise.nc: output file from GP in 2D space and RBF kernel.2D_prediction_MAT32_fix_noise.nc: output file from GP in 2D space and MAT32 kernel.prediction_MAT32_various_input.nc: output file from GP in 8D space and MAT32 kernel.theory_theory_8D_fix_noise.nc: output file from GP in 8D space and MAT32 kernel using FRDM12 as training data.