Fast multi‐physics simulation approach in underwater exploration via deep learning technique. Issue 5 (15th December 2021)

• Record Type: Journal Article
• Authors: Zhu, Yue; Zhou, Yuanguo; Javaid, Fawad; Ren, Qiang; Liu, Wenyuan
• Is Part Of: Electronics letters. Volume 58:Issue 5(2022)
• Page Start: 200
• Page End: 202
• Publication Date: 2021-12-15
• Subjects: Electronics -- Periodicals; 621.381
• DOI: 10.1049/ell2.12406
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract: 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.