  Most active supermassive black holes (SMBH) in present-day galaxies are
underfed and consist of low-luminosity active galactic nuclei (LLAGN). They
have multiwavelength broadband spectral energy distributions (SED) dominated by
non-thermal processes which are quite different from those of the brighter,
more distant quasars. Modelling the observed SEDs of LLAGNs is currently
challenging, given the large computational expenses required. In this work, we
used machine learning (ML) methods to generate model SEDs and fit sparse
observations of LLAGNs. Our ML model consisted of a neural network and
reproduced with excellent precision the radio-to-X-rays emission from a
radiatively inefficient accretion flow around a SMBH and a relativistic jet, at
a small fraction of the computational cost. The ML method performs the fit $4
\times 10^5$ times faster than previous semianalytic models. As a
proof-of-concept, we used the ML model to reproduce the SEDs of the LLAGNs M87,
NGC 315 and NGC 4261.
