Machine learning provides great opportunities for accelerating the
representation of physical processes in weather and climate models. Radiative
transfer solvers, which deal with the transport of solar and thermal radiation,
are often the most costly parametrization in models. Speeding them up is one of
the main ambitions of SLOCS [https:
