AN OUT-OF-DISTRIBUTION-AWARE AUTOENCODER MODEL FOR REDUCED CHEMICAL KINETICS

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Abstract

While detailed chemical kinetic models have been successful in representing rates of chemical reactions in continuum scale computational fluid dynamics (CFD) simulations, applying the models in simulations for engineering device conditions is computationally prohibitive. To reduce the cost, data-driven methods, e.g., autoencoders, have been used to construct reduced chemical kinetic models for CFD simulations. Despite their success, data-driven methods rely heavily on training data sets and can be unreliable when used in out-of-distribution (OOD) regions (i.e., when extrapolating outside of the training set). In this paper, we present an enhanced autoencoder model for combustion chemical kinetics with uncertainty quantification to enable the detection of model usage in OOD regions, and thereby creating an OOD-aware autoencoder model that contributes to more robust CFD simulations of reacting flows. We first demonstrate the effectiveness of the method in OOD detection in two well-known datasets, MNIST and Fashion-MNIST, in comparison with the deep ensemble method, and then present the OOD-aware autoencoder for reduced chemistry model in syngas combustion.

Original languageEnglish
Pages (from-to)913-930
Number of pages18
JournalDiscrete and Continuous Dynamical Systems - Series S
Volume15
Issue number4
DOIs
StatePublished - Apr 2022

Funding

Acknowledgments. This material is based upon work supported in part by the U.S. Department of Energy, Office of Science, Offices of Advanced Scientific Computing Research, under the contract ERKJ352, and by the AI Initiative at the Oak Ridge National Laboratory (ORNL). ORNL is operated by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • Chemical kinetics
  • dimension reduction
  • neural network
  • out of distribution
  • uncertainty quantification

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