  Models of physics beyond the Standard Model often contain a large number of
parameters. These form a high-dimensional space that is computationally
intractable to fully explore. Experimental constraints project onto a subspace
of viable parameters, but mapping these constraints to the underlying
parameters is also typically intractable. Instead, physicists often resort to
scanning small subsets of the full parameter space and testing for experimental
consistency. We propose an alternative approach that uses generative models to
significantly improve the computational efficiency of sampling high-dimensional
parameter spaces. To demonstrate this, we sample the constrained and
phenomenological Minimal Supersymmetric Standard Models subject to the
requirement that the sampled points are consistent with the measured Higgs
boson mass. Our method achieves orders of magnitude improvements in sampling
efficiency compared to a brute force search.
