17. Bibliography

The present bibliography is made of an explicit choice of didactic references, often introductory but not only, and as far as possible publicly accessible. These references accompany the learning process as well as the advanced use of the methods available in the module, without the intention of constituting an exhaustive bibliography.

[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
[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., Mathematical Analysis and Numerical Methods for Science and Technology, Tome 1 à 6, Springer, 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
[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://ab-initio.mit.edu/nlopt
[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., Optimal Control of Systems Governed by Partial Differential Equations, Springer, 1971
[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
[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, 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, 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 and Code_Aster, Analysis of Structures and Thermomechanics for Studies & Research, http://www.code-aster.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
[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
[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
[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