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

@article{Kim2022AFA,
  title={A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder},
  author={Youngkyu Kim and Youngsoo Choi and David Peter Widemann and Tarek Zohdi},
  journal={J. Comput. Phys.},
  year={2022},
  volume={451},
  pages={110841}
}
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61 Citations
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61 Citations

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This work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena.

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BT-AE framework, a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches, relies on the combination of an autoencoder and Barlow Twins self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture.

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds

BT-AE framework, a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches, relies on the combination of an autoencoder and Barlow Twins self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture.
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References

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