Building Surrogate Models of Nuclear Density Functional Theory with Gaussian Processesand Autoencoders

Abstract

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z=118), it is estimated that as many as about 3000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like $\beta$ or $\gamma$ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

Submitted to Research Topic "Uncertainty Quantification in Nuclear Physics" of Frontiers in Physics

References in corpus (21)

• Marginalized Denoising Autoencoders for Domain Adaptation
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
• Theory of Nuclear Fission
• Intrinsic-Density Functionals
• Solution of the Skyrme HF+BCS equation on a 3D mesh. II. A new version of the Ev8 code
• Nuclear masses learned from a probabilistic neural network
• Physically Interpretable Machine Learning for nuclear masses
• Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
• Skyrme-Hartree-Fock-Bogoliubov mass models on a 3D Mesh: Effect of triaxial shape
• Validating neural-network refinements of nuclear mass models
• The time-dependent generator coordinate method in nuclear physics
• Model reduction methods for nuclear emulators
• A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model
• Axially-deformed solution of the Skyrme-Hartree-Fock-Bogoliubov equations using the transformed harmonic oscillator basis (IV) hfbtho (v4.0): A new version of the program
• Quantifying uncertainties in neutron-alpha scattering with chiral nucleon-nucleon and three-nucleon forces
• Density functional theory for self-bound systems
• A new statistical method for the structure of the inner crust of neutron stars
• Smoothing of one- and two-dimensional discontinuities in potential energy surfaces
• Microscopic Description of Induced Fission
• A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functionals

Cited by in corpus (2)

• Neural Network Emulation of Spontaneous Fission
• Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data