暂无分享,去创建一个
Dirk Blömker | Claudia Schillings | Philipp Wacker | Simon Weissmann | D. Blömker | Philipp Wacker | Simon Weissmann | C. Schillings
[1] Otmar Scherzer,et al. On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems , 2021, Numerische Mathematik.
[2] R. Khasminskii. Stochastic Stability of Differential Equations , 1980 .
[3] Claudia Schillings,et al. Well Posedness and Convergence Analysis of the Ensemble Kalman Inversion , 2018 .
[4] R. Tempone,et al. Multilevel ensemble Kalman filtering for spatially extended models , 2016, 1608.08558.
[5] Sebastian Reich,et al. A mollified ensemble Kalman filter , 2010, 1002.3091.
[6] Xin T. Tong,et al. Nonlinear stability and ergodicity of ensemble based Kalman filters , 2015, 1507.08307.
[7] Haakon Hoel,et al. Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators , 2019 .
[8] Hamidou Tembine,et al. Deterministic Mean-Field Ensemble Kalman Filtering , 2014, SIAM J. Sci. Comput..
[9] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[10] Nikolas Nüsken,et al. Affine invariant interacting Langevin dynamics for Bayesian inference , 2020, SIAM J. Appl. Dyn. Syst..
[11] Andrew Stuart,et al. Convergence analysis of ensemble Kalman inversion: the linear, noisy case , 2017, 1702.07894.
[12] Marco A. Iglesias,et al. Iterative regularization for ensemble data assimilation in reservoir models , 2014, Computational Geosciences.
[13] Andrew M. Stuart,et al. Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations , 2002, SIAM J. Numer. Anal..
[14] Wilhelm Stannat,et al. Long-Time Stability and Accuracy of the Ensemble Kalman-Bucy Filter for Fully Observed Processes and Small Measurement Noise , 2016, SIAM J. Appl. Dyn. Syst..
[15] W. Stannat,et al. Mean field limit of Ensemble Square Root filters - discrete and continuous time , 2020, Foundations of Data Science.
[16] P. Kloeden,et al. Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients , 2010, 1010.3756.
[17] Michael Herty,et al. A Stabilization of a Continuous Limit of the Ensemble Kalman Filter , 2020, ArXiv.
[18] Qin Li,et al. Ensemble Kalman Inversion for nonlinear problems: weights, consistency, and variance bounds , 2020, ArXiv.
[19] Theresa Lange,et al. On the continuous time limit of the ensemble Kalman filter , 2019, Math. Comput..
[20] Andrew M. Stuart,et al. Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler , 2019, SIAM J. Appl. Dyn. Syst..
[21] Andrew M. Stuart,et al. Tikhonov Regularization within Ensemble Kalman Inversion , 2019, SIAM J. Numer. Anal..
[22] Qin Li,et al. Ensemble Kalman inversion: mean-field limit and convergence analysis , 2019, Statistics and Computing.
[23] F. Gland,et al. Large sample asymptotics for the ensemble Kalman filter , 2009 .
[24] Marco A. Iglesias,et al. A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems , 2015, 1505.03876.
[25] G. Evensen. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .
[26] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[27] P. Kloeden,et al. Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[28] Andrew J. Majda,et al. Performance of Ensemble Kalman Filters in Large Dimensions , 2016, 1606.09321.
[29] V. Morozov. On the solution of functional equations by the method of regularization , 1966 .
[30] D. Oliver,et al. Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother , 2011, Mathematical Geosciences.
[31] A. Chambolle,et al. An introduction to Total Variation for Image Analysis , 2009 .
[32] A. Stuart,et al. Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time , 2013, 1310.3167.
[33] Pierre Del Moral,et al. On the stability and the uniform propagation of chaos properties of Ensemble Kalman-Bucy filters , 2016, 1605.09329.
[34] Theresa Lange,et al. On the continuous time limit of ensemble square root filters , 2021, Communications in Mathematical Sciences.
[35] Dirk Blömker,et al. A Strongly Convergent Numerical Scheme from Ensemble Kalman Inversion , 2017, SIAM J. Numer. Anal..
[36] A. Stuart,et al. Ensemble Kalman methods for inverse problems , 2012, 1209.2736.
[37] Claudia Schillings,et al. On the Incorporation of Box-Constraints for Ensemble Kalman Inversion , 2019, Foundations of Data Science.
[38] Sebastian Reich,et al. Fokker-Planck particle systems for Bayesian inference: Computational approaches , 2019, SIAM/ASA J. Uncertain. Quantification.
[39] Martin Burger,et al. Modern regularization methods for inverse problems , 2018, Acta Numerica.
[40] Andrew M. Stuart,et al. Ensemble Kalman inversion: a derivative-free technique for machine learning tasks , 2018, Inverse Problems.
[41] Neil K. Chada,et al. Multilevel Ensemble Kalman-Bucy Filters , 2020, SIAM/ASA J. Uncertain. Quantification.
[42] S. Reich. A dynamical systems framework for intermittent data assimilation , 2011 .
[43] Kody J. H. Law,et al. Multilevel ensemble Kalman filtering , 2015, SIAM J. Numer. Anal..
[44] Jan Mandel,et al. Convergence of the Square Root Ensemble Kalman Filter in the Large Ensemble Limit , 2014, SIAM/ASA J. Uncertain. Quantification.
[45] S. Reich,et al. A localization technique for ensemble Kalman filters , 2009, 0909.1678.
[47] H. Engl,et al. Convergence rates for Tikhonov regularisation of non-linear ill-posed problems , 1989 .
[48] Albert C. Reynolds,et al. Ensemble smoother with multiple data assimilation , 2013, Comput. Geosci..
[49] Yuchen Yang,et al. Adaptive regularisation for ensemble Kalman inversion , 2020 .
[50] Andrew M. Stuart,et al. Analysis of the Ensemble Kalman Filter for Inverse Problems , 2016, SIAM J. Numer. Anal..
[51] Oliver G. Ernst,et al. Analysis of the Ensemble and Polynomial Chaos Kalman Filters in Bayesian Inverse Problems , 2015, SIAM/ASA J. Uncertain. Quantification.
[52] Michael Herty,et al. Kinetic methods for inverse problems , 2018, Kinetic & Related Models.
[53] M. Hutzenthaler,et al. Numerical Approximations of Stochastic Differential Equations With Non-globally Lipschitz Continuous Coefficients , 2012, 1203.5809.