Imperial College London

DrBogdanUngureanu

Faculty of Natural SciencesDepartment of Mathematics

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6m10Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Aznavourian:2022:epjam/2021011,
author = {Aznavourian, R and Guenneau, S and Ungureanu, B and Marot, J},
doi = {epjam/2021011},
journal = {EPJ Applied Metamaterials},
title = {Morphing for faster computations with finite difference time domain algorithms},
url = {http://dx.doi.org/10.1051/epjam/2021011},
volume = {9},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - In the framework of wave propagation, finite difference time domain (FDTD) algorithms, yield high computational time. We propose to use morphing algorithms to deduce some approximate wave pictures of their interactions with fluid-solid structures of various shapes and different sizes deduced from FDTD computations of scattering by solids of three given shapes: triangular, circular and elliptic ones. The error in the L2 norm between the FDTD solution and approximate solution deduced via morphing from the source and destination images are typically less than 1% if control points are judiciously chosen. We thus propose to use a morphing algorithm to deduce approximate wave pictures: at intermediate time steps from the FDTD computation of wave pictures at a time step before and after this event, and at the same time step, but for an average frequency signal between FDTD computation of wave pictures with two different signal frequencies. We stress that our approach might greatly accelerate FDTD computations as discretizations in space and time are inherently linked via the Courant–Friedrichs–Lewy stability condition. Our approach requires some human intervention since the accuracy of morphing highly depends upon control points, but compared to the direct computational method our approach is much faster and requires fewer resources. We also compared our approach to some neural style transfer (NST) algorithm, which is an image transformation method based on a neural network. Our approach outperforms NST in terms of the L2 norm, Mean Structural SIMilarity, expected signal to error ratio.
AU - Aznavourian,R
AU - Guenneau,S
AU - Ungureanu,B
AU - Marot,J
DO - epjam/2021011
PY - 2022///
SN - 2272-2394
TI - Morphing for faster computations with finite difference time domain algorithms
T2 - EPJ Applied Metamaterials
UR - http://dx.doi.org/10.1051/epjam/2021011
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000750540000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://epjam.edp-open.org/articles/epjam/full_html/2022/01/epjam210011/epjam210011.html
UR - http://hdl.handle.net/10044/1/94827
VL - 9
ER -