  We present two methods, based on Chebyshev tensors, to compute dynamic
sensitivities of financial instruments within a Monte Carlo simulation. These
methods are implemented and run in a Monte Carlo engine to compute Dynamic
Initial Margin as defined by ISDA (SIMM). We show that the levels of accuracy,
speed and implementation efforts obtained, compared to the benchmark (DIM
obtained calling pricing functions such as are found in risk engines), are
better than those obtained by alternative methods presented in the literature,
such as regressions (\cite{Zhu Chan}) and Deep Neural Nets (\cite{DNNs IM}).
