19. 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

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://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

NumPy20

Harris C.R. et al., Array programming with NumPy, Nature 585, pp.357–362, 2020, https://numpy.org/

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, 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/

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

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

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