[1] Jonas Adler and Ozan Öktem. Learned primal-dual reconstruction. IEEE transactions on medical imaging, 37(6):1322-1332, 2018.
[2] Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems, 29, 2016.
[3] D Behringer, T Birdsall, M Brown, B Cornuelle, R Heinmiller, R Knox, K Metzger, W Munk, J Spiesberger, R Spindel, D Webb, P Worcester, and C Wunsch. A demonstration of ocean acoustic tomography. Nature, 299(5879):121-125, 1982.
[4] Matthew Chantry, Sam Hatfield, Peter Dueben, Inna Polichtchouk, and Tim Palmer. Machine learning emulation of gravity wave drag in numerical weather forecasting. Journal of Advances in Modeling Earth Systems, 13(7):e2021MS002477, 2021.
[5] Yang Deng, Simiao Ren, Kebin Fan, Jordan M Malof, and Willie J Padilla. Neural-adjoint method for the inverse design of all-dielectric metasurfaces. Optics Express, 29(5):7526-7534, 2021.
[6] David R Dowling and Karim G Sabra. Acoustic remote sensing. Annual Review of Fluid Mechanics, 47:221-243, 2015.
[7] Nicholas C Durofchalk, Jihui Jin, Heriberto J Vazquez, Kay L Gemba, Justin Romberg, and Karim G Sabra. Data driven source localization using a library of nearby shipping sources of opportunity. JASA Express Letters, 1(12):124802, 2021.
[8] Geir Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5):10143–10162, 1994.
[9] Zalan Fabian and Mahdi Soltanolkotabi. Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction. arXiv preprint arXiv:2203.08213, 2022.
[10] Peimeng Guan, Jihui Jin, Justin Romberg, and Mark A Davenport. Loop unrolled shallow equilibrium regularizer (luser)-a memory-efficient inverse problem solver. arXiv preprint arXiv:2210.04987, 2022.
[11] Sam Hatfield, Matthew Chantry, Peter Dueben, Philippe Lopez, Alan Geer, and Tim Palmer. Building tangent-linear and adjoint models for data assimilation with neural networks. Journal of Advances in Modeling Earth Systems, 13(9):e2021MS002521, 2021.
[12] Peter L Houtekamer and Herschel L Mitchell. Data assimilation using an ensemble kalman filter technique. Monthly Weather Review, 126(3):796-811, 1998.
[13] Zhaoqiong Huang, Ji Xu, Zaixiao Gong, Haibin Wang, and Yonghong Yan. Source localization using deep neural networks in a shallow water environment. The journal of the acoustical society of America, 143(5):2922-2932, 2018.
[14] Finn B Jensen, William A Kuperman, Michael B Porter, Henrik Schmidt, and Alexandra Tolstoy. Computational Ocean Acoustics, volume 794. Springer, 2011.
[15] Jihui Jin, Priyabrata Saha, Nicholas Durofchalk, Saibal Mukhopadhyay, Justin Romberg, and Karim G Sabra. Machine learning approaches for ray-based ocean acoustic tomography. The Journal of the Acoustical Society of America, 152(6):3768-3788, 2022.
[16] George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422-440, 2021.
[17] Kenneth Levenberg. A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2(2):164-168, 1944.
[18] Dong C Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1-3):503-528, 1989.