  The detailed detector simulation models are vital for the successful
operation of modern high-energy physics experiments. In most cases, such
detailed models require a significant amount of computing resources to run.
Often this may not be afforded and less resource-intensive approaches are
desired. In this work, we demonstrate the applicability of Generative
Adversarial Networks (GAN) as the basis for such fast-simulation models for the
case of the Time Projection Chamber (TPC) at the MPD detector at the NICA
accelerator complex. Our prototype GAN-based model of TPC works more than an
order of magnitude faster compared to the detailed simulation without any
noticeable drop in the quality of the high-level reconstruction characteristics
for the generated data. Approaches with direct and indirect quality metrics
optimization are compared.
