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In this work, sequence-to-sequence (seq2seq) models, originally developed forlanguage translation, are used to predict the temporal evolution of complex,multi-physics computer simulations. The predictive performance of seq2seqmodels is compared to state transition models for datasets generated withmulti-physics codes with varying levels of complexity - from simple 1Ddiffusion calculations to simulations of inertial confinement fusionimplosions. Seq2seq models demonstrate the ability to accurately emulatecomplex systems, enabling the rapid estimation of the evolution of quantitiesof interest in computationally expensive simulations.

Predicting the time-evolution of multi-physics systems with sequence-to-sequence models