Genetic Programming for the Nuclear Many-Body Problem: a Guide

TL;DR

This paper demonstrates how Genetic Programming can create surrogate models that significantly accelerate complex nuclear many-body calculations with minimal accuracy loss.

Contribution

It introduces a method to develop reduced order models using Genetic Programming to efficiently approximate nuclear many-body computations.

Findings

Speed-up of computations by several orders of magnitude

Negligible loss in accuracy of surrogate models

Effective application to models with self-consistent mean fields

Abstract

Genetic Programming is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use Genetic Programming to develop surrogate models to mitigate the computational costs of modeling atomic nuclei with ever increasing complexity. The computational burden escalates when uncertainty quantification is pursued, or when observables must be globally computed for thousands of nuclei. By studying three models in which the mean field depends on the total particle density self-consistently, we show that by constructing reduced order models supported by Genetic Programming one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy