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James Keeble

Postgraduate Research Student

Academic and research departments

Department of Physics.

My publications


Keeble J.W.T., Rios A. (2020) Machine learning the deuteron,Physics Letters, Section B 809 135743 Elsevier
We use machine learning techniques to solve the nuclear two-body bound state problem, the deuteron. We use a minimal one-layer, feed-forward neural network to represent the deuteron S- and D-state wavefunction in momentum space, and solve the problem variationally using ready-made machine learning tools. We benchmark our results with exact diagonalisation solutions. We ?nd that a network with 6 hidden nodes (or 24 parameters) can provide a faithful representation of the ground state wavefunction, with a binding energy that is within 0.1% of exact results. This exploratory proof-of principle simulation may provide insight for future potential solutions of the nuclear many-body problem using variational arti?cial neural network techniques.