  The Mpemba effect describes the phenomenon that a system at a hot initial
temperature cools faster than at an initial warm temperature in the same
environment. Such an anomalous cooling has recently been predicted and realized
for trapped colloids. Here, we investigate the freezing behavior of a passive
colloidal particle by employing numerical Brownian dynamics simulations and
theoretical calculations with a model that can be directly tested in
experiments. During the cooling process, the colloidal particle exhibits
multiple non-monotonic regimes in cooling rates, with the cooling time
decreasing twice as a function of the initial temperature-an unexpected
phenomenon we refer to as the Double Mpemba effect. Additionally, we
demonstrate that both the Mpemba and Double Mpemba effects can be predicted by
various machine learning methods, which expedite the analysis of complex,
computationally intensive systems.
