  Solar Energetic Particles (SEPs) form a critical component of Space Weather.
The complex, intertwined dynamics of SEP sources, acceleration, and transport
make their forecasting very challenging. Yet, information about SEP arrival and
their properties (e.g., peak flux) is crucial for space exploration on many
fronts. We have recently introduced a novel probabilistic ensemble model called
the Multivariate Ensemble of Models for Probabilistic Forecast of Solar
Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence
and physical properties of SEPs. The occurrence forecasting, thoroughly
discussed in a preceding paper (Chatterjee et al., 2023), is complemented by
the work presented here, which focuses on forecasting the physical properties
of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural
Networks, which leverage a multi-variate dataset comprising full-disc
magnetogram sequences and numerous derived and in-situ data from various
sources. Skill scores demonstrate that MEMPSEP exhibits improved predictions on
SEP properties for the test set data with SEP occurrence probability above 50%,
compared to those with a probability below 50%. Results present a promising
approach to address the challenging task of forecasting SEP physical
properties, thus improving our forecasting capabilities and advancing our
understanding of the dominant parameters and processes that govern SEP
production.
