  In the present work, the Tensor-Train decomposition algorithm is applied to
reduce the memory footprint of a stochastic discrete velocity solver for
rarefied gas dynamics simulation. An energy-conserving modification to the
algorithm is proposed, along with an interleaved collision/convection routine
which allows for easy application of higher-order convection schemes. The
performance of the developed algorithm is analyzed for several 0- and
1-dimensional model problems in terms of solution error and reduction in memory
use requirements.
