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Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available.
A kalman filter analysis of sea level height in the tropical pacific
R. N. Miller and M. A. Cane · 1989
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Nonlinear principal component analysis using autoassociative neural networks
M. A. Kramer · 1991
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Predictability: a problem partly solved
E. Lorenz · 1995
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Approximate data assimilation schemes for stable and unstable dynamics
S. E. Cohn and R. Todling · 1996
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A singular evolutive extended kalman filter for data assimilation in oceanography
D. Tuan Pham, J. Verron, and M. Christine Roubaud · 1998
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Wavelet transform adapted to an approximate kalman filter system
A. Tangborn and S. Q. Zhang · 2000
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Assimilation of standard and targeted observations within the unstable subspace of the observation–analysis–forecast cycle system
A. Trevisan and F. Uboldi · 2004
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Detecting unstable structures and controlling error growth by assimilation of standard and adaptive observations in a primitive equation ocean model
F. Uboldi and A. Trevisan · 2006
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A reduced-order approach to four-dimensional variational data assimilation using proper orthogonal decomposition
Y. Cao, J. Zhu, I. M. Navon, and Z. Luo · 2007
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Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system
A. Carrassi, A. Trevisan, and F. Uboldi · 2007
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Sequential data assimilation with sigma-point kalman filter on low-dimensional manifold
Z. Lu, T. K. Leen, R. van der Merwe, S. Frolov, and A. M. Baptista · 2007
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A review of forecast error covariance statistics in atmospheric variational data assimilation. i: Characteristics and measurements of forecast error covariances
R. N. Bannister · 2008
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Controlling instabilities along a 3dvar analysis cycle by assimilating in the unstable subspace: a comparison with the enkf
A. Carrassi, A. Trevisan, L. Descamps, O. Talagrand, and F. Uboldi · 2008
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Four-dimensional variational assimilation in the unstable subspace and the optimal subspace dimension
A. Trevisan, M. D’Isidoro, and O. Talagrand · 2010
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On the kalman filter error covariance collapse into the unstable subspace
A. Trevisan and L. Palatella · 2011
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Strong and weak constraint variational assimilations for reduced order fluid flow modeling
G. Artana, A. Cammilleri, J. Carlier, and E. Mémin · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Building high-level features using large scale unsupervised learning
Q. V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
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An iterative ensemble kalman smoother
M. Bocquet and P. Sakov · 2014
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Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks
C. Kuenzer, M. Ottinger, M. Wegmann, H. Guo, C. Wang, J. Zhang, S. Dech, and M. Wikelski · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Accounting for model error due to unresolved scales within ensemble kalman filtering
L. Mitchell and A. Carrassi · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Data assimilation: methods, algorithms, and applications
M. Asch, M. Bocquet, and M. Nodet · 2016
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Data assimilation: methods, algorithms, and applications
M. Asch, M. Bocquet, and M. Nodet · 2016
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Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Hybrid levenberg–marquardt and weak-constraint ensemble kalman smoother method
Latent-space physics: Towards learning the temporal evolution of fluid flow
S. Wiewel, M. Becher, and N. Thuerey · 2018
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Lecture notes, 2014, last revision: January 2019
M. Bocquet · 2019
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Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother
S. W. Canchumuni, A. A. Emerick, and M. A. C. Pacheco · 2019
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Latent-space dynamics for reduced deformable simulation
L. Fulton, V. Modi, D. Duvenaud, D. I. W. Levin, and A. Jacobson · 2019
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Imexnet: A forward stable deep neural network, 2019
E. Haber, K. Lensink, E. Treister, and L. Ruthotto · 2019
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J. Mandel, E. Bergou, S. Gürol, S. Gratton, and I. Kasanický · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Johnson, X. Liu, L. Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, and J. Dean · 2016
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A weak-constraint 4densemblevar. part i: formulation and simple model experiments
J. Amezcua, M. Goodliff, and P. J. V. Leeuwen · 2017
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Four-dimensional ensemble variational data assimilation and the unstable subspace
M. Bocquet and A. Carrassi · 2017
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Stable architectures for deep neural networks
E. Haber and L. Ruthotto · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. Qi, H. Su, K. Mo, and L. Guibas · 2017
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M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat · 2019
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Differentiable physics-informed graph networks
S. Seo and Y. Liu · 2019
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Informed machine learning – a taxonomy and survey of integrating knowledge into learning systems
L. von Rüden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, and J. Schücker · 2019
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Rezero is all you need: Fast convergence at large depth, 2020
T. Bachlechner, B. P. Majumder, H. H. Mao, G. W. Cottrell, and J. McAuley · 2020
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Outlook for exploiting artificial intelligence in the earth and environmental sciences
S.-A. Boukabara, V. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. McGovern, D. Hall, J. E. T. Hoeve, J. Hickey, H.-L. A. Huang, J. K. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. J. Geer, E. S. Maddy, and R. N. Hoffman · 2020
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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the lorenz 96 model
J. Brajard, A. Carrassi, M. Bocquet, and L. Bertino · 2020
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An iterative ensemble kalman smoother in presence of additive model error
A. Fillion, M. Bocquet, S. Gratton, S. Gürol, and P. Sakov · 2020
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Solving parametric pde problems with artificial neural networks
Y. Khoo, J. Lu, and L. Ying · 2020
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Fourier Neural Operator for Parametric Partial Differential Equations
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2020
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Attention-based convolutional autoencoders for 3d-variational data assimilation
J. Mack, R. Arcucci, M. Molina-Solana, and Y. Guo · 2020
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Embedding hard physical constraints in neural network coarse-graining of 3d turbulence
A. Mohan, N. Lubbers, D. Livescu, and M. Chertkov · 2020
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A survey of the usages of deep learning for natural language processing
D. W. Otter, J. R. Medina, and J. K. Kalita · 2020
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Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows
S. M. Pawar and O. San · 2020
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Latent space subdivision: Stable and controllable time predictions for fluid flow
S. Wiewel, B. Kim, V. C. Azevedo, B. Solenthaler, and N. Thuerey · 2020
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Learning earth system models from observations: machine learning or data assimilation?
A. J. Geer · 2021
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Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
R. Maulik, B. Lusch, and P. Balaprakash · 2021
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Detection of iterative adversarial attacks via counter attack, 2021
M. Rottmann, K. Maag, M. Peyron, N. Krejic, and H. Gottschalk · 2021
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