19. Bibliographie¶

La présente bibliographie est constituée d’un choix explicite de références à visée didactique, souvent introductives mais pas uniquement, et dans la mesure du possible publiquement accessibles. Ces références accompagnent la prise en main comme l’usage avancé des méthodes disponibles dans le module, sans néanmoins d’intention de constituer une bibliographie exhaustive.

Argaud09: Argaud J.-P., Bouriquet B., Hunt J., Data Assimilation from Operational and Industrial Applications to Complex Systems, Mathematics Today, pp.150-152, October 2009
Asch16: Asch M., Bocquet M., Nodet M., Data Assimilation - Methods, Algorithms and Applications, SIAM, 2016
Barrault04: Barrault M., Maday Y., Nguyen N. C., Patera A. T., An “empirical interpolation” method: application to efficient reduced-basis discretization of partial differential equations, Comptes Rendus Mathématique, 339(9), pp.667–672, 2004
Bishop01: Bishop C. H., Etherton B. J., Majumdar S. J., Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects, Monthly Weather Review, 129, pp.420–436, 2001
Bocquet04: Bocquet M., Introduction aux principes et méthodes de l’assimilation de données en géophysique, Lecture Notes, 2014
Bouttier99: Bouttier B., Courtier P., Data assimilation concepts and methods, Meteorological Training Course Lecture Series, ECMWF, 1999
Buchinsky98: Buchinsky M., Recent Advances in Quantile Regression Models: A Practical Guidline for Empirical Research, Journal of Human Resources, 33(1), pp.88-126, 1998
Burgers98: Burgers G., Van Leuween P. J., Evensen G., Analysis scheme in the Ensemble Kalman Filter, Monthly Weather Review, 126(6), pp.1719–1724, 1998
Byrd95: Byrd R. H., Lu P., Nocedal J., A Limited Memory Algorithm for Bound Constrained Optimization, SIAM Journal on Scientific and Statistical Computing, 16(5), pp.1190-1208, 1995
Cade03: Cade B. S., Noon B. R., A Gentle Introduction to Quantile Regression for Ecologists, Frontiers in Ecology and the Environment, 1(8), pp.412-420, 2003
Chakraborty08: Chakraborty U.K., Advances in differential evolution, Studies in computational intelligence, Vol.143, Springer, 2008
Chaturantabut10: Chaturantabut S., Sorensen D.C., Nonlinear model reduction via discrete empirical interpolation, SIMA Journal of Scientific Computing, 32(5), pp.2737-2764, 2010
Cohn98: Cohn S. E., Da Silva A., Guo J., Sienkiewicz M., Lamich D., Assessing the effects of data selection with the DAO Physical-space Statistical Analysis System, Monthly Weather Review, 126, pp.2913–2926, 1998
Courtier94: Courtier P., Thépaut J.-N., Hollingsworth A., A strategy for operational implementation of 4D-Var, using an incremental approach, Quarterly Journal of the Royal Meteorological Society, 120(519), pp.1367–1387, 1994
Courtier97: Courtier P., Dual formulation of four-dimensional variational assimilation, Quarterly Journal of the Royal Meteorological Society, 123(544), pp.2249-2261, 1997
Das11: Das S., Suganthan P. N., Differential Evolution: A Survey of the State-of-the-art, IEEE Transactions on Evolutionary Computation, 15(1), pp.4-31, 2011
Das16: Das S., Mullick S. S., Suganthan P. N., Recent Advances in Differential Evolution - An Updated Survey, Swarm and Evolutionary Computation, 27, pp.1-30, 2016
Dautray85: Dautray R., Lions J.-L., et al., Analyse mathématique et calcul numérique pour les sciences et les techniques, Tome 1 à 9, Masson, 1985-1988
Evensen94: Evensen G., Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal of Geophysical Research, 99(C5), pp.10143–10162, 1994
Evensen03: Evensen G., The Ensemble Kalman Filter: theoretical formulation and practical implementation, Seminar on Recent developments in data assimilation for atmosphere and ocean, ECMWF, 8 to 12 September 2003
GilBellosta15: Gil Bellosta C. J., rPython: Package Allowing R to Call Python, CRAN, 2015, https://cran.r-project.org/web/packages/rPython/ and http://rpython.r-forge.r-project.org/
Glover89: Glover F., Tabu Search-Part I, ORSA Journal on Computing, 1(2), pp.190-206, 1989
Glover90: Glover F., Tabu Search-Part II, ORSA Journal on Computing, 2(1), pp.4-32, 1990
Gnuplot: Gnuplot - Portable command-line driven graphing utility, http://www.gnuplot.info/
Gnuplot.py: Gnuplot.py - A pipe-based interface to the gnuplot plotting program, http://gnuplot-py.sourceforge.net
Gong18: Gong H., Data assimilation with reduced basis and noisy measurement: Applications to nuclear reactor cores, PhD Thesis, Sorbonne Université (France), 2018
Hamill00: Hamill T. M., Snyder C., A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme, Monthly Weather Review, 128(8), pp.2905-2919, 2000
Ide97: Ide K., Courtier P., Ghil M., Lorenc A. C., Unified notation for data assimilation: operational, sequential and variational, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997
Jazwinski70: Jazwinski A. H., Stochastic Processes and Filtering Theory, Academic Press, 1970
Johnson08: Johnson S. G., The NLopt nonlinear-optimization package, http://github.com/stevengj/nlopt
Julier95: Julier S., Uhlmann J., Durrant-Whyte H., A new approach for filtering nonlinear systems, in: Proceedings of the 1995 American Control Conference, IEEE, 1995
Julier00: Julier S., Uhlmann J., Durrant-Whyte H., A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Trans. Automat. Control., 45, pp.477–482, 2000
Julier07: Julier S., Laviola J., On Kalman ﬁltering with nonlinear equality constraints, IEEE Trans. Signal Process., 55(6), pp.2774-2784, 2007
Kalnay03: Kalnay E., Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003
Koenker00: Koenker R., Hallock K. F., Quantile Regression: an Introduction, 2000, http://www.econ.uiuc.edu/~roger/research/intro/intro.html
Koenker01: Koenker R., Hallock K. F., Quantile Regression, Journal of Economic Perspectives, 15(4), pp.143-156, 2001
LeDimet86: Le Dimet F.-X., Talagrand 0., Variational algorithms for analysis and assimilation of meteorological observations, Tellus, 38A, pp.97-110, 1986
Lions68: Lions J.-L., Contrôle optimal de systèmes gouvernés par des équations aux dérivées partielles, Dunod, 1968
Lorenc86: Lorenc A. C., Analysis methods for numerical weather prediction, Quarterly Journal of the Royal Meteorological Society, 112(474), pp.1177-1194, 1986
Lorenc88: Lorenc A. C., Optimal nonlinear objective analysis, Quarterly Journal of the Royal Meteorological Society, 114(479), pp.205–240, 1988
Morales11: Morales J. L., Nocedal J., L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization, ACM Transactions on Mathematical Software, 38(1), 2011
Nelder65: Nelder J. A., Mead R., A simplex method for function minimization, The Computer Journal, 7, pp.308-313, 1965
NumPy20: Harris C. R. et al., Array programming with NumPy, Nature 585, pp.357–362, 2020, https://numpy.org/
Papakonstantinou22: Papakonstantinou K. G., Amir M., Warn G. P., A Scaled Spherical Simplex Filter (S3F) with a decreased n+2 sigma points set size and equivalent 2n+1 Unscented Kalman Filter (UKF) accuracy, Mechanical Systems and Signal Processing, 163, 107433, 2022
Powell64: Powell M. J. D., An efficient method for finding the minimum of a function of several variables without calculating derivatives, Computer Journal, 7(2), pp.155-162, 1964
Powell94: Powell M. J. D., A direct search optimization method that models the objective and constraint functions by linear interpolation, in Advances in Optimization and Numerical Analysis, eds. S. Gomez and J-P Hennart, Kluwer Academic (Dordrecht), pp. 51-67, 1994
Powell98: Powell M. J. D., Direct search algorithms for optimization calculations, Acta Numerica 7, pp.287-336, 1998
Powell04: Powell M. J. D., The NEWUOA software for unconstrained optimization without derivatives, Proc. 40th Workshop on Large Scale Nonlinear Optimization, Erice, Italy, 2004
Powell07: Powell M. J. D., A view of algorithms for optimization without derivatives, Cambridge University Technical Report DAMTP 2007/NA03, 2007
Powell09: Powell M. J. D., The BOBYQA algorithm for bound constrained optimization without derivatives, Cambridge University Technical Report DAMTP NA2009/06, 2009
Price05: Price K.V., Storn R., Lampinen J., Differential evolution: a practical approach to global optimization, Springer, 2005
Python: Python programming language, http://www.python.org/
Quarteroni16: Quarteroni A., Manzoni A., Negri F., Reduced Basis Methods for Partial Differential Equations - An introduction, Unitext vol.92, Springer, 2016
R: The R Project for Statistical Computing, http://www.r-project.org/
Rowan90: Rowan T., Functional Stability Analysis of Numerical Algorithms, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990
Salome: SALOME The Open Source Integration Platform for Numerical Simulation, http://www.salome-platform.org/
SalomeMeca: Salome_Meca et Code_Aster, Analyse des Structures et Thermomécanique pour les Etudes et la Recherche, http://www.code-aster.org/
SciPy20: Virtanen P. et al., SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17(3), pp.261-272, 2020, https://scipy.org/
Storn97: Storn R., Price, K., Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11(1), pp.341-359, 1997
Tarantola87: Tarantola A., Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation, Elsevier, 1987
Talagrand97: Talagrand O., Assimilation of Observations, an Introduction, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997
Tikhonov77: Tikhonov A. N., Arsenin V. Y., Solution of Ill-posed Problems, Winston & Sons, 1977
Wan00: Wan E. A., van der Merwe R., The Unscented Kalman Filter for Nonlinear Estimation, in: Adaptive Systems for Signal Processing, Communications, and Control Symposium, IEEE, 2000.
Welch06: Welch G., Bishop G., An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Department of Computer Science, TR 95-041, 2006, http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf
WikipediaDA: Wikipedia, Data assimilation, http://en.wikipedia.org/wiki/Data_assimilation
WikipediaKF: Wikipedia, Kalman Filter, https://en.wikipedia.org/wiki/Kalman_filter
WikipediaEKF: Wikipedia, Extended Kalman Filter, https://en.wikipedia.org/wiki/Extended_Kalman_filter
WikipediaEnKF: Wikipedia, Ensemble Kalman Filter, http://en.wikipedia.org/wiki/Ensemble_Kalman_filter
WikipediaMO: Wikipedia, Mathematical optimization, https://en.wikipedia.org/wiki/Mathematical_optimization
WikipediaND: Wikipedia, Nondimensionalization, https://en.wikipedia.org/wiki/Nondimensionalization
WikipediaNM: Wikipedia, Nelder–Mead method, https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
WikipediaPSO: Wikipedia, Particle Swarm Optimization, https://en.wikipedia.org/wiki/Particle_swarm_optimization
WikipediaQR: Wikipedia, Quantile regression, https://en.wikipedia.org/wiki/Quantile_regression
WikipediaTI: Wikipedia, Tikhonov regularization, https://en.wikipedia.org/wiki/Tikhonov_regularization
WikipediaTS: Wikipedia, Tabu search, https://en.wikipedia.org/wiki/Tabu_search
WikipediaUKF: Wikipedia, Unscented Kalman Filter, https://en.wikipedia.org/wiki/Unscented_Kalman_filter
ZambranoBigiarini13: Zambrano-Bigiarini M., Clerc M., Rojas R., Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements, 2013 IEEE Congress on Evolutionary Computation, pp.2337-2344, 2013
Zhu97: Zhu C., Byrd R. H., Nocedal J., L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization, ACM Transactions on Mathematical Software, 23(4), pp.550-560, 1997
Zupanski05: Zupanski M., Maximum likelihood ensemble filter: Theoretical aspects, Monthly Weather Review, 133(6), pp.1710–1726, 2005

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