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• W4294631254 endingPage "111592" @default.
• W4294631254 startingPage "111592" @default.
• W4294631254 abstract "We approximate the underwater acoustic wave problem for locating sources in that medium. We create a time dependent synthetic data-set of sensor recorded pressures, based on a small set of sensors placed in the domain, and perturb this data with high random multiplicative noise. We show that reference time-reversal based method struggles with high noise, and a naive deep-learning method also fails. We propose a method, based on physically-informed neural-networks and time-reversal, for approximating the source location even in the presence of high sensors noise." @default.
• W4294631254 created "2022-09-05" @default.
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• W4294631254 date "2022-12-01" @default.
• W4294631254 modified "2023-10-18" @default.
• W4294631254 title "A physically-informed deep-learning model using time-reversal for locating a source from sparse and highly noisy sensors data" @default.
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• W4294631254 doi "https://doi.org/10.1016/j.jcp.2022.111592" @default.
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