We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.
%0 Journal Article
%1 BlaSU21b
%A Black, Felix
%A Schulze, Philipp
%A Unger, Benjamin
%D 2021
%I MDPI AG
%J Fluids
%K exc2075 myown pn4
%N 8
%P 280
%R 10.3390/fluids6080280
%T Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction
%V 6
%X We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.
@article{BlaSU21b,
abstract = {We propose a new hyper-reduction method for a recently introduced nonlinear model reduction framework based on dynamically transformed basis functions and especially well-suited for transport-dominated systems. Furthermore, we discuss applying this new method to a wildland fire model whose dynamics feature traveling combustion waves and local ignition and is thus challenging for classical model reduction schemes based on linear subspaces. The new hyper-reduction framework allows us to construct parameter-dependent reduced-order models (ROMs) with efficient offline/online decomposition. The numerical experiments demonstrate that the ROMs obtained by the novel method outperform those obtained by a classical approach using the proper orthogonal decomposition and the discrete empirical interpolation method in terms of run time and accuracy.},
added-at = {2021-08-18T10:53:36.000+0200},
author = {Black, Felix and Schulze, Philipp and Unger, Benjamin},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24da122e6eb37ed24bc10966724ee46e0/benjaminunger},
doi = {10.3390/fluids6080280},
interhash = {a634588eb54daaf4fd1c173f167d49c6},
intrahash = {4da122e6eb37ed24bc10966724ee46e0},
journal = {Fluids},
keywords = {exc2075 myown pn4},
number = 8,
pages = 280,
publisher = {{MDPI} {AG}},
timestamp = {2022-02-07T14:24:32.000+0100},
title = {Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction},
volume = 6,
year = 2021
}