[19] Guangpeng Liu, Annalisa Bracco, and Uta Passow. The influence of mesoscale and submesoscale circulation on sinking particles in the northern gulf of mexico. *Elementa: science of the Anthropocene*, 6, 2018.
[20] Guangpeng Liu, Annalisa Bracco, and Alexandra Sitar. Submesoscale mixing across the mixed layer in the gulf of mexico. Frontiers in Marine Science, 8:615066, 2021.
[21] Xiaofeng Liu, Yalan Song, and Chaopeng Shen. Bathymetry inversion using a deep-learning-based surrogate for shallow water equations solvers. arXiv preprint arXiv:2203.02821, 2022.
[22] Wei Ma, Feng Cheng, and Yongmin Liu. Deep-learning-enabled on-demand design of chiral metamaterials. ACS nano, 12(6):6326-6334, 2018.
[23] Donald W Marquardt. An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2):431-441, 1963.
[24] Herman Medwin and Clarence S Clay. Fundamentals of Acoustical Oceanography. Elsevier, 1998.
[25] Ali Mousavi and Richard G Baraniuk. Learning to invert: Signal recovery via deep convolutional networks. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 2272-2276. IEEE, 2017.
[26] Walter Munk and Carl Wunsch. Ocean acoustic tomography: A scheme for large scale monitoring. Deep Sea Research Part A. Oceanographic Research Papers, 26(2):123-161, 1979.
[27] Walter H Munk and Peter F Worcester. Ocean acoustic tomography. Oceanography, 1(1):8-10, 1988.
[28] Christian C Nadell, Bohao Huang, Jordan M Malof, and Willie J Padilla. Deep learning for accelerated all-dielectric metasurface design. Optics express, 27(20):27523-27535, 2019.
[29] Haiqiang Niu, Emma Ozanich, and Peter Gerstoft. Ship localization in Santa barbara channel using machine learning classifiers. The journal of the acoustical society of America, 142(5):EL455-EL460, 2017.
[30] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.
[31] John Peurifoy, Yichen Shen, Li Jing, Yi Yang, Fidel Cano-Renteria, Brendan G DeLacy, John D Joannopoulos, Max Tegmark, and Marin Soljačić. Nanophotonic particle simulation and inverse design using artificial neural networks. Science advances, 4(6):eaar4206, 2018.
[32] Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686-707, 2019.
[33] Simiao Ren, Willie Padilla, and Jordan Malof. Benchmarking deep inverse models over time, and the neural-adjoint method. Advances in Neural Information Processing Systems, 33:38-48, 2020.
[34] EK Skarsoulis, GA Athanassoulis, and Uwe Send. Ocean acoustic tomography based on peak arrivals. The Journal of the Acoustical Society of America, 100(2):797-813, 1996.
[35] Jian Sun, Huibin Li, Zongben Xu, et al. Deep admm-net for compressive sensing mri. Advances in neural information processing systems, 29, 2016.
[36] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[37] Bo Wang and Huafeng Liu. Fbp-net for direct reconstruction of dynamic pet images. Physics in Medicine & Biology, 65(23):235008, 2020.