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

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 multiphysics 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. Keywords: recurrent neural network, sequence-to-sequence, multiphysics simulation, radiation hydrodynamics 1. Introduction Computer simulations of detailed multi-physics systems often take several hours to run, making exploration throughout a vast design space prohibitively expensive. A common method for mapping design spaces of large computer codes is to train a machine learning model to emulate the code in a region of the design space [1, 2, 3]. The machine learning model, often called a "surrogate", D. Humbird) Preprint submitted to Elsevier November 15, 2018 learns to accurately interpolate between a set of simulations spread throughout the reduced design space, such that it can rapidly predict quantities of interest anywhere within that space without requiring additional expensive simulations.