Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where “practical” refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios and effectively learn from incomplete and biased prior physics and noisy dataset. Experiments demonstrate SPINNs' advantages over PINN in practical acoustic propagation estimation. The learning capacity of SPINNs toward physics and dataset during training is further analyzed. This work holds promise for practical applications and future expansion.
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May 16 2024
Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics
Jie Duan;
Jie Duan
1
College of Information Science and Electronic Engineering, Zhejiang University
, Hangzhou, Zhejiang 310027, China
2
Interdisciplinary Student Training Platform for Marine Areas, Zhejiang University
, Zhoushan, Zhejiang 316021, China
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Hangfang Zhao
;
Hangfang Zhao
a)
1
College of Information Science and Electronic Engineering, Zhejiang University
, Hangzhou, Zhejiang 310027, China
3
Key Laboratory of Ocean Observation–Imaging Testbed of Zhejiang Province
, Zhoushan, Zhejiang 316021, China
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Jinbao Song
Jinbao Song
4
Ocean College, Zhejiang University
, Zhoushan, Zhejiang 316021, China
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a)
Email: hfzhao@zju.edu.cn
J. Acoust. Soc. Am. 155, 3306–3321 (2024)
Article history
Received:
January 09 2024
Accepted:
April 30 2024
Citation
Jie Duan, Hangfang Zhao, Jinbao Song; Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics. J. Acoust. Soc. Am. 1 May 2024; 155 (5): 3306–3321. https://doi.org/10.1121/10.0026025
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