  In this work, sequence-to-sequence (seq2seq) models, originally developed for
language translation, are used to predict the temporal evolution of complex,
multi-physics computer simulations. The predictive performance of seq2seq
models is compared to state transition models for datasets generated with
multi-physics codes with varying levels of complexity - from simple 1D
diffusion calculations to simulations of inertial confinement fusion
implosions. Seq2seq models demonstrate the ability to accurately emulate
complex systems, enabling the rapid estimation of the evolution of quantities
of interest in computationally expensive simulations.
