2 Copyright (C) 2008-2024 EDF R&D
4 This file is part of SALOME ADAO module.
6 This library is free software; you can redistribute it and/or
7 modify it under the terms of the GNU Lesser General Public
8 License as published by the Free Software Foundation; either
9 version 2.1 of the License, or (at your option) any later version.
11 This library is distributed in the hope that it will be useful,
12 but WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 Lesser General Public License for more details.
16 You should have received a copy of the GNU Lesser General Public
17 License along with this library; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
20 See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
22 Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 .. _section_bibliography:
26 ================================================================================
28 ================================================================================
30 The present bibliography is made of an explicit choice of didactic references,
31 often introductory but not only, and as far as possible publicly accessible.
32 These references accompany the learning process as well as the advanced use of
33 the methods available in the module, without the intention of constituting an
34 exhaustive bibliography.
36 .. [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
38 .. [Asch16] Asch M., Bocquet M., Nodet M., *Data Assimilation - Methods, Algorithms and Applications*, SIAM, 2016
40 .. [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
42 .. [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
44 .. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2014
46 .. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999
48 .. [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
50 .. [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
52 .. [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
54 .. [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
56 .. [Chakraborty08] Chakraborty U.K., *Advances in differential evolution*, Studies in computational intelligence, Vol.143, Springer, 2008
58 .. [Chaturantabut10] Chaturantabut S., Sorensen D.C., *Nonlinear model reduction via discrete empirical interpolation*, SIMA Journal of Scientific Computing, 32(5), pp.2737-2764, 2010
60 .. [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
62 .. [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
64 .. [Courtier97] Courtier P., *Dual formulation of four-dimensional variational assimilation*, Quarterly Journal of the Royal Meteorological Society, 123(544), pp.2249-2261, 1997
66 .. [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
68 .. [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
70 .. [Dautray85] Dautray R., Lions J.-L., et al., *Mathematical Analysis and Numerical Methods for Science and Technology*, Tome 1 à 6, Springer, 1988
72 .. [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
74 .. [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
76 .. [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/
78 .. [Glover89] Glover F., *Tabu Search-Part I*, ORSA Journal on Computing, 1(2), pp.190-206, 1989
80 .. [Glover90] Glover F., *Tabu Search-Part II*, ORSA Journal on Computing, 2(1), pp.4-32, 1990
82 .. [Gnuplot] *Gnuplot - Portable command-line driven graphing utility*, http://www.gnuplot.info/
84 .. [Gnuplot.py] *Gnuplot.py - A pipe-based interface to the gnuplot plotting program*, http://gnuplot-py.sourceforge.net
86 .. [Gong18] Gong H., *Data assimilation with reduced basis and noisy measurement: Applications to nuclear reactor cores*, PhD Thesis, Sorbonne Université (France), 2018
88 .. [Hamill00] Hamill T. M., Snyder C., *A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme*, Monthly Weather Review, 128(8), pp.2905-2919, 2000
90 .. [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
92 .. [Jazwinski70] Jazwinski A. H., *Stochastic Processes and Filtering Theory*, Academic Press, 1970
94 .. [Johnson08] Johnson S. G., *The NLopt nonlinear-optimization package*, http://github.com/stevengj/nlopt
96 .. [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
98 .. [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
100 .. [Julier07] Julier S., Laviola J., *On Kalman filtering with nonlinear equality constraints*, IEEE Trans. Signal Process., 55(6), pp.2774-2784, 2007
102 .. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003
104 .. [Koenker00] Koenker R., Hallock K. F., *Quantile Regression: an Introduction*, 2000, http://www.econ.uiuc.edu/~roger/research/intro/intro.html
106 .. [Koenker01] Koenker R., Hallock K. F., *Quantile Regression*, Journal of Economic Perspectives, 15(4), pp.143-156, 2001
108 .. [LeDimet86] Le Dimet F.-X., Talagrand 0., *Variational algorithms for analysis and assimilation of meteorological observations*, Tellus, 38A, pp.97-110, 1986
110 .. [Lions68] Lions J.-L., *Optimal Control of Systems Governed by Partial Differential Equations*, Springer, 1971
112 .. [Lorenc86] Lorenc A. C., *Analysis methods for numerical weather prediction*, Quarterly Journal of the Royal Meteorological Society, 112(474), pp.1177-1194, 1986
114 .. [Lorenc88] Lorenc A. C., *Optimal nonlinear objective analysis*, Quarterly Journal of the Royal Meteorological Society, 114(479), pp.205–240, 1988
116 .. [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
118 .. [Nelder65] Nelder J. A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965
120 .. [NumPy20] Harris C. R. et al., *Array programming with NumPy*, Nature 585, pp.357–362, 2020, https://numpy.org/
122 .. [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
124 .. [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
126 .. [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
128 .. [Powell98] Powell M. J. D., *Direct search algorithms for optimization calculations*, Acta Numerica 7, pp.287-336, 1998
130 .. [Powell04] Powell M. J. D., *The NEWUOA software for unconstrained optimization without derivatives*, Proc. 40th Workshop on Large Scale Nonlinear Optimization, Erice, Italy, 2004
132 .. [Powell07] Powell M. J. D., *A view of algorithms for optimization without derivatives*, Cambridge University Technical Report DAMTP 2007/NA03, 2007
134 .. [Powell09] Powell M. J. D., *The BOBYQA algorithm for bound constrained optimization without derivatives*, Cambridge University Technical Report DAMTP NA2009/06, 2009
136 .. [Price05] Price K.V., Storn R., Lampinen J., *Differential evolution: a practical approach to global optimization*, Springer, 2005
138 .. [Python] *Python programming language*, http://www.python.org/
140 .. [Quarteroni16] Quarteroni A., Manzoni A., Negri F., *Reduced Basis Methods for Partial Differential Equations - An introduction*, Unitext vol.92, Springer, 2016
142 .. [R] *The R Project for Statistical Computing*, http://www.r-project.org/
144 .. [Rowan90] Rowan T., *Functional Stability Analysis of Numerical Algorithms*, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990
146 .. [Salome] *SALOME The Open Source Integration Platform for Numerical Simulation*, http://www.salome-platform.org/
148 .. [SalomeMeca] *Salome_Meca and Code_Aster, Analysis of Structures and Thermomechanics for Studies & Research*, http://www.code-aster.org/
150 .. [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/
152 .. [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
154 .. [Tarantola87] Tarantola A., *Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation*, Elsevier, 1987
156 .. [Talagrand97] Talagrand O., *Assimilation of Observations, an Introduction*, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997
158 .. [Tikhonov77] Tikhonov A. N., Arsenin V. Y., *Solution of Ill-posed Problems*, Winston & Sons, 1977
160 .. [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.
162 .. [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
164 .. [WikipediaDA] Wikipedia, *Data assimilation*, http://en.wikipedia.org/wiki/Data_assimilation
166 .. [WikipediaKF] Wikipedia, *Kalman Filter*, https://en.wikipedia.org/wiki/Kalman_filter
168 .. [WikipediaEKF] Wikipedia, *Extended Kalman Filter*, https://en.wikipedia.org/wiki/Extended_Kalman_filter
170 .. [WikipediaEnKF] Wikipedia, *Ensemble Kalman Filter*, http://en.wikipedia.org/wiki/Ensemble_Kalman_filter
172 .. [WikipediaMO] Wikipedia, *Mathematical optimization*, https://en.wikipedia.org/wiki/Mathematical_optimization
174 .. [WikipediaND] Wikipedia, *Nondimensionalization*, https://en.wikipedia.org/wiki/Nondimensionalization
176 .. [WikipediaNM] Wikipedia, *Nelder–Mead method*, https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
178 .. [WikipediaPSO] Wikipedia, *Particle Swarm Optimization*, https://en.wikipedia.org/wiki/Particle_swarm_optimization
180 .. [WikipediaQR] Wikipedia, *Quantile regression*, https://en.wikipedia.org/wiki/Quantile_regression
182 .. [WikipediaTI] Wikipedia, *Tikhonov regularization*, https://en.wikipedia.org/wiki/Tikhonov_regularization
184 .. [WikipediaTS] Wikipedia, *Tabu search*, https://en.wikipedia.org/wiki/Tabu_search
186 .. [WikipediaUKF] Wikipedia, *Unscented Kalman Filter*, https://en.wikipedia.org/wiki/Unscented_Kalman_filter
188 .. [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
190 .. [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
192 .. [Zupanski05] Zupanski M., *Maximum likelihood ensemble filter: Theoretical aspects*, Monthly Weather Review, 133(6), pp.1710–1726, 2005