  We propose an active sampling flow, with the use-case of simulating the
impact of combined variations on analog circuits. In such a context, given the
large number of parameters, it is difficult to fit a surrogate model and to
efficiently explore the space of design features.
  By combining a drastic dimension reduction using sensitivity analysis and
Bayesian surrogate modeling, we obtain a flexible active sampling flow. On
synthetic and real datasets, this flow outperforms the usual Monte-Carlo
sampling which often forms the foundation of design space exploration.
