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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 existin 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 supernovaexplosions or neutron star mergers that are presumed to be at the origin ofmost heavy elements in the Universe. Understanding the structure, reactions,and decays of nuclei across the entire chart of nuclides is an enormouschallenge because of the experimental difficulties in measuring properties ofinterest in such fleeting objects and the theoretical and computational issuesof simulating strongly-interacting quantum many-body systems. Nuclear densityfunctional theory (DFT) is a fully microscopic theoretical framework which hasthe potential of providing such a quantitatively accurate description ofnuclear properties for every nucleus in the chart of nuclides. Thanks tohigh-performance computing facilities, it has already been successfully appliedto 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 nuclearspectroscopy or of nuclear fission, or the quantification of theoreticaluncertainties and their propagation to applications, would require severalorders of magnitude more calculations than currently possible. However, most ofthis computational effort would be spent into generating a suitable basis ofDFT wavefunctions. Such a task could potentially be considerably accelerated byborrowing tools from the field of machine learning and artificial intelligence.In this paper, we review different approaches to applying supervised andunsupervised learning techniques to nuclear DFT.

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