Non-parametric Bayesian approach to extrapolation problems in configuration interaction methods

Abstract

We propose a non-parametric extrapolation method based on constrained Gaussian processes for configuration interaction methods. Our method has many advantages: (i) applicability to small data sets such as results of {\it ab initio} methods, (ii) flexibility to incorporate constraints, which are guided by physics, into the extrapolation model, (iii) providing predictions with quantified extrapolation uncertainty, etc. In the present study, we show an application to the extrapolation needed in full configuration interaction method as an example.

arXiv comment11 pages and 12 figures

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Cited by in corpus (5)

• Machine Learning in Nuclear Physics
• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Ground-state properties of light $4n$ self-conjugate nuclei in $ab$ $initio$ no-core Monte Carlo shell model calculations with nonlocal $NN$ interactions
• Spectroscopic factor calculations in the \textit{ab initio} no-core shell model
• Report from the A.I. For Nuclear Physics Workshop