Open AccessFast multi‐physics simulation approach in underwater exploration via deep learning technique
Author(s) -
Zhu Yue,
Zhou Yuanguo,
Javaid Fawad,
Ren Qiang,
Liu Wenyuan
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12406
Subject(s) - underwater , attenuation , artificial neural network , computer science , process (computing) , range (aeronautics) , field (mathematics) , physics , acoustics , computational physics , aerospace engineering , artificial intelligence , optics , engineering , geology , mathematics , oceanography , pure mathematics , operating system
When underwater pressure wave is generated, moving water particles cut off the geomagnetic field and produce induced currents, which will simultaneously induce electromagnetic field in the whole space. Due to the large distribution range and slow attenuation of this pseudo‐radiation, it is possible to observe above the sea surface. In this work, we introduce a novel long‐ and short‐term memory neural network and the corresponding training algorithm, to model the multi‐physics process instead of solving magneto‐hydrodynamics equations via numerical methods. Compared with commercial software, the proposed approach is much faster and easier to apply, which puts forward a feasible alternative for predicting the electromagnetic field distribution excited by underwater pressure wave.