  In this study, we developed a quantitative description of the dynamics of
spin-torque vortex nano-oscillators (STVOs) through an unconventional model
based on the combination of the Thiele equation approach (TEA) and data from
micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical
model allows to accelerate the simulations by 9 orders of magnitude compared to
MMS while reaching the same level of accuracy. Here, we showcase our model by
simulating a STVO-based neural network for solving a classification task. We
assess its performance with respect to the input signal current intensity and
the level of noise that might affect such a system. Our approach is promising
for accelerating the design of STVO-based neuromorphic computing devices while
decreasing drastically its computational cost.
