Morphing for faster computations with finite difference time domain algorithms - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EPJ Applied Metamaterials Année : 2022

Morphing for faster computations with finite difference time domain algorithms

Ronald Aznavourian
  • Fonction : Auteur correspondant
  • PersonId : 961611

Connectez-vous pour contacter l'auteur
Sébastien Guenneau
Julien Marot
  • Fonction : Auteur

Résumé

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.
Fichier principal
Vignette du fichier
epjam210011.pdf (17.68 Mo) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-03556307 , version 1 (03-02-2022)

Identifiants

Citer

Ronald Aznavourian, Sébastien Guenneau, Bogdan Ungureanu, Julien Marot. Morphing for faster computations with finite difference time domain algorithms. EPJ Applied Metamaterials, 2022, 9, pp.2. ⟨10.1051/epjam/2021011⟩. ⟨hal-03556307⟩
29 Consultations
6 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More