Corpus ID: 221949087

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

@article{Kim2020AFA,
  title={A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder},
  author={Youngkyu Kim and Y. Choi and David Widemann and T. Zohdi},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.11990}
}

Youngkyu Kim, Y. Choi, +1 author T. Zohdi

Published 2020

Computer Science, Mathematics

ArXiv

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena, such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases… CONTINUE READING

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