Extrapolating from neural network models: a cautionary tale

Abstract

We present three different methods to estimate error bars on the predictions made using a neural network. All of them represent lower bounds for the extrapolation errors. For example, we did not include an analysis on robustness against small perturbations of the input data. At first, we illustrate the methods through a simple toy model, then, we apply them to some realistic cases related to nuclear masses. By using theoretical data simulated either with a liquid-drop model or a Skyrme energy density functional, we benchmark the extrapolation performance of the neural network in regions of the Segrè chart far away from the ones used for the training and validation. Finally, we discuss how error bars can help identifying when the extrapolation becomes too uncertain and thus unreliable

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Cited by in corpus (5)

• Machine Learning in Nuclear Physics
• Physically Interpretable Machine Learning for nuclear masses
• Application of multilayer perceptron with data augmentation in nuclear physics
• Controlling extrapolations of nuclear properties with feature selection
• From nuclei to neutron stars: simple binding energy computer modelling in the classroom (Part 1)