79 citations as recorded by crossref.

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5. Stable explicit stepwise marching scheme in ill-posed time-reversed 2D Burgers' equation A. Carasso https://doi.org/10.1080/17415977.2018.1523905
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17. Back-and-forth nudging moving horizon estimation for discrete-time linear systems J. Xie & B. Huang https://doi.org/10.1016/j.automatica.2024.111691
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20. Optimal Feedback Force Locations in Measurement‐Integrated Simulation for Two‐Dimensional Flow Around a Circular Cylinder K. Hirose & S. Miyauchi https://doi.org/10.1002/fld.70045
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22. A new back‐and‐forth iterative method for time‐reversed convection modeling: Implications for the Cenozoic evolution of 3‐D structure and dynamics of the mantle P. Glišović & A. Forte https://doi.org/10.1002/2016JB012841
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26. Joint Estimation of Balanced Motions and Internal Tides From Future Wide‐Swath Altimetry F. Le Guillou et al. https://doi.org/10.1029/2021MS002613
27. Enhanced retrospective forecasting in dissipative dynamical systems using transformer and multi-scale ESRGAN models M. Zhang et al. https://doi.org/10.1016/j.rineng.2024.103597
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29. Effect of discrete time observations on synchronization in Chua model and applications to data assimilation M. Nurujjaman et al. https://doi.org/10.1063/1.4712591
30. Derivative‐free optimization for large‐scale nonlinear data assimilation problems S. Gratton et al. https://doi.org/10.1002/qj.2169
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35. Sequential identification of boundary support parameters in a fluid-structure vascular model using patient image data P. Moireau et al. https://doi.org/10.1007/s10237-012-0418-3
36. Downscaling data assimilation algorithm with applications to statistical solutions of the Navier–Stokes equations C. Mondaini et al. https://doi.org/10.1016/j.anihpc.2018.05.004
37. Data Assimilation in Large Prandtl Rayleigh--Bénard Convection from Thermal Measurements A. Farhat et al. https://doi.org/10.1137/19M1248327
38. Recovering the observable part of the initial data of an infinite-dimensional linear system with skew-adjoint generator G. Haine https://doi.org/10.1007/s00498-014-0124-z
39. Continuous data assimilation for two-phase flow: Analysis and simulations Y. Chow et al. https://doi.org/10.1016/j.jcp.2022.111395
40. The Inverse Problem for Hamilton--Jacobi Equations and Semiconcave Envelopes C. Esteve & E. Zuazua https://doi.org/10.1137/20M1330130
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42. Diffusive Back and Forth Nudging algorithm for data assimilation D. Auroux et al. https://doi.org/10.1016/j.crma.2011.07.004
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44. A Spatially Dependent Nudging Method and Its Application to a Global Tide Assimilation L. Liu & X. Chen https://doi.org/10.1007/s00376-024-4062-5
45. Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model F. Le Guillou et al. https://doi.org/10.1175/JTECH-D-20-0104.1
46. Data Assimilation for the Navier--Stokes Equations Using Local Observables A. Biswas et al. https://doi.org/10.1137/20M136058X
47. Reconstructing the initial state for the nonlinear system and analyzing its convergence W. Xie & Y. Chang https://doi.org/10.1186/1687-1847-2014-82
48. Back-and-forth nudging for the quasi-geostrophic ocean dynamics with altimetry: Theoretical convergence study and numerical experiments with the future SWOT observations S. Amraoui et al. https://doi.org/10.3934/dcdss.2022058
49. Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations A. Carrassi et al. https://doi.org/10.5194/npg-21-521-2014
50. Reconstructing the Initial State of the Linear System and Estimating its Error in Discretization R. Wang & W. Xie https://doi.org/10.11650/tjm/221105
51. An easy-to-implement and efficient data assimilation method for the identification of the initial condition: the Back and Forth Nudging (BFN) algorithm D. Auroux et al. https://doi.org/10.1088/1742-6596/135/1/012011
52. Uniform exponential stabilization and the state reconstruction of the wave equation with viscosity Z. Fu et al. https://doi.org/10.1360/SSM-2020-0292
53. State reconstruction of a distributed parameter system with exact observability G. Xu https://doi.org/10.1016/j.jmaa.2013.06.014
54. An evolution of the back and forth nudging for geophysical data assimilation: application to Burgers equation and comparisons D. Auroux et al. https://doi.org/10.1080/17415977.2012.712528
55. Finite time synchronization of the continuous/discrete data assimilation algorithms for Lorenz 63 system based on the back and forth nudging techniques Y. Peng et al. https://doi.org/10.1016/j.rinam.2023.100407
56. Ocean‐depth measurement using shallow‐water wave models V. Vasan et al. https://doi.org/10.1111/sapm.12418
57. Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart D. Chapelle et al. https://doi.org/10.1007/s11517-012-0969-6
58. IMU-Enhanced Vessel Trajectory Prediction: Overcoming Kinematic Lag and Distribution Shift in Sparse Data Engineering Scenarios Z. Zhang et al. https://doi.org/10.3390/jmse14050461
59. First results of operational ionospheric dynamics prediction for the Brazilian Space Weather program A. Petry et al. https://doi.org/10.1016/j.asr.2014.03.017
60. Eccentricity Parameters Identification for a Motorized Spindle System Based on Improved Maximum Likelihood Method W. Mao et al. https://doi.org/10.1155/2020/5491957
61. On the physical nudging equations G. Conti et al. https://doi.org/10.1007/s00382-021-05972-w
62. Dynamic Initialization for Whole Atmospheric Global Modeling I. Song et al. https://doi.org/10.1029/2017MS001213
63. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
64. Continuous Data Assimilation for the Three-Dimensional Navier--Stokes Equations A. Biswas & R. Price https://doi.org/10.1137/20M1323229
65. Artificial Intelligence techniques in data assimilation: Emerging approaches, key challenges, and future prospects W. Wang et al. https://doi.org/10.1007/s11430-025-1807-8
66. A time reversal based algorithm for solving initial data inverse problems K. Ito et al. https://doi.org/10.3934/dcdss.2011.4.641
67. Continuous data assimilation for the 3D Ladyzhenskaya model: analysis and computations Y. Cao et al. https://doi.org/10.1016/j.nonrwa.2022.103659
68. Data assimilation in 2D hyperbolic/parabolic systems using a stabilized explicit finite difference scheme run backward in time A. Carasso https://doi.org/10.1080/27690911.2023.2282641
69. Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean V. Shutyaev https://doi.org/10.1134/S0001433819010080
70. Output error minimizing back and forth nudging method for initial state recovery A. Aalto https://doi.org/10.1016/j.sysconle.2016.06.002
71. Sources of skill in near-term climate prediction: generating initial conditions A. Carrassi et al. https://doi.org/10.1007/s00382-016-3036-4
72. Object-Based Analog Forecasts for Surface Wind Speed M. Frediani et al. https://doi.org/10.1175/MWR-D-17-0012.1
73. An efficient data assimilation algorithm using the Allen–Cahn equation B. Xia et al. https://doi.org/10.1016/j.enganabound.2023.06.029
74. Data assimilation with model errors A. Çıbık et al. https://doi.org/10.1016/j.camwa.2026.04.023
75. Data assimilation with regularized nonlinear instabilities H. Abarbanel et al. https://doi.org/10.1002/qj.600
76. A Switching Observer for a Class of Nonuniformly Observable Systems via Singular Time-Rescaling M. Aguado-Rojas et al. https://doi.org/10.1109/TAC.2021.3062340
77. Model and observation‐error covariance matrix information in the physical nudging equations G. Conti et al. https://doi.org/10.1002/qj.4979
78. Luenberger Observers for Infinite-Dimensional Systems, Back and Forth Nudging, and Application to a Crystallization Process L. Brivadis et al. https://doi.org/10.1137/20M1329020
79. Symmetry-Based Observers for Some Water-Tank Problems D. Auroux & S. Bonnabel https://doi.org/10.1109/TAC.2010.2067291