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.
Argaud J.-P., Bouriquet B., Hunt J., Data Assimilation from Operational and Industrial Applications to Complex Systems, Mathematics Today, pp.150-152, October 2009
Asch M., Bocquet M., Nodet M., Data Assimilation - Methods, Algorithms and Applications, SIAM, 2016
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
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
Bocquet M., Introduction aux principes et méthodes de l’assimilation de données en géophysique, Lecture Notes, 2014
Bouttier B., Courtier P., Data assimilation concepts and methods, Meteorological Training Course Lecture Series, ECMWF, 1999
Buchinsky M., Recent Advances in Quantile Regression Models: A Practical Guidline for Empirical Research, Journal of Human Resources, 33(1), pp.88-126, 1998
Burgers G., Van Leuween P. J., Evensen G., Analysis scheme in the Ensemble Kalman Filter, Monthly Weather Review, 126(6), pp.1719–1724, 1998
Wikipedia, Butterfly effect, https://en.wikipedia.org/wiki/Butterfly_effect
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
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
Chakraborty U.K., Advances in differential evolution, Studies in computational intelligence, Vol.143, Springer, 2008
Chaturantabut S., Sorensen D.C., Nonlinear model reduction via discrete empirical interpolation, SIMA Journal of Scientific Computing, 32(5), pp.2737-2764, 2010
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
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
Courtier P., Dual formulation of four-dimensional variational assimilation, Quarterly Journal of the Royal Meteorological Society, 123(544), pp.2249-2261, 1997
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
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
Dautray R., Lions J.-L., et al., Mathematical Analysis and Numerical Methods for Science and Technology, Tome 1 à 6, Springer, 1988
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
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
Gil Bellosta C. J., rPython: Package Allowing R to Call Python, CRAN, 2015, https://cran.r-project.org/web/packages/rPython/ and https://rpython.r-forge.r-project.org/
Glover F., Tabu Search-Part I, ORSA Journal on Computing, 1(2), pp.190-206, 1989
Glover F., Tabu Search-Part II, ORSA Journal on Computing, 2(1), pp.4-32, 1990
Gnuplot - Portable command-line driven graphing utility, http://www.gnuplot.info/
Gnuplot.py - A pipe-based interface to the gnuplot plotting program, https://gnuplot-py.sourceforge.net
Gong H., Data assimilation with reduced basis and noisy measurement: Applications to nuclear reactor cores, PhD Thesis, Sorbonne Université (France), 2018
Hamill T. M., Snyder C., A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme, Monthly Weather Review, 128(8), pp.2905-2919, 2000
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
Jazwinski A. H., Stochastic Processes and Filtering Theory, Academic Press, 1970
Johnson S. G., The NLopt nonlinear-optimization package, https://github.com/stevengj/nlopt
Julier S., Uhlmann J., Durrant-Whyte H., A new approach for filtering nonlinear systems, in: Proceedings of the 1995 American Control Conference, IEEE, 1995
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
Julier S., Laviola J., On Kalman filtering with nonlinear equality constraints, IEEE Trans. Signal Process., 55(6), pp.2774-2784, 2007
Kalnay E., Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2003
Kirkpatrick S., Gelatt C. D., Vecchi M. P., Optimization by Simulated Annealing, Science, 220 (4598), pp.671–680, 1983
Koenker R., Hallock K. F., Quantile Regression: an Introduction, 2000, http://www.econ.uiuc.edu/~roger/research/intro/intro.html
Koenker R., Hallock K. F., Quantile Regression, Journal of Economic Perspectives, 15(4), pp.143-156, 2001
Le Dimet F.-X., Talagrand 0., Variational algorithms for analysis and assimilation of meteorological observations, Tellus, 38A, pp.97-110, 1986
Lions J.-L., Optimal Control of Systems Governed by Partial Differential Equations, Springer, 1971
Lorenc A. C., Analysis methods for numerical weather prediction, Quarterly Journal of the Royal Meteorological Society, 112(474), pp.1177-1194, 1986
Lorenc A. C., Optimal nonlinear objective analysis, Quarterly Journal of the Royal Meteorological Society, 114(479), pp.205–240, 1988
Lorenz E. N., Deterministic nonperiodic flow, Journal of the Atmospheric Sciences, 20, pp.130–141, 1963 https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
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
Nelder J. A., Mead R., A simplex method for function minimization, The Computer Journal, 7, pp.308-313, 1965
Harris C. R. et al., Array programming with NumPy, Nature, 585, pp.357–362, 2020, https://numpy.org/
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
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
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
Powell M. J. D., Direct search algorithms for optimization calculations, Acta Numerica 7, pp.287-336, 1998
Powell M. J. D., The NEWUOA software for unconstrained optimization without derivatives, Proc. 40th Workshop on Large Scale Nonlinear Optimization, Erice, Italy, 2004
Powell M. J. D., A view of algorithms for optimization without derivatives, Cambridge University Technical Report DAMTP 2007/NA03, 2007
Powell M. J. D., The BOBYQA algorithm for bound constrained optimization without derivatives, Cambridge University Technical Report DAMTP NA2009/06, 2009
Price K.V., Storn R., Lampinen J., Differential evolution: a practical approach to global optimization, Springer, 2005
Python programming language, https://www.python.org/
Quarteroni A., Manzoni A., Negri F., Reduced Basis Methods for Partial Differential Equations - An introduction, Unitext vol.92, Springer, 2016
The R Project for Statistical Computing, https://www.r-project.org/
Rowan T., Functional Stability Analysis of Numerical Algorithms, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin, 1990
SALOME The Open Source Integration Platform for Numerical Simulation, https://www.salome-platform.org/
Salome_Meca and Code_Aster, Analysis of Structures and Thermomechanics for Studies & Research, https://www.code-aster.org/
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/
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
Tarantola A., Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation, Elsevier, 1987
Talagrand O., Assimilation of Observations, an Introduction, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997
Tikhonov A. N., Arsenin V. Y., Solution of Ill-posed Problems, Winston & Sons, 1977
Tsallis C., Stariolo D.A., Generalized Simulated Annealing, Physica A, 233, pp.395-406, 1996
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
Wang G.-Y., Han D.-X., Particle Swarm Optimization Based on Self-adaptive Acceleration Factors, in: Third International Conference on Genetic and Evolutionary Computing, pp.637-640, 2009
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, https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf
Wikipedia, Data assimilation, https://en.wikipedia.org/wiki/Data_assimilation
Wikipedia, Kalman Filter, https://en.wikipedia.org/wiki/Kalman_filter
Wikipedia, Extended Kalman Filter, https://en.wikipedia.org/wiki/Extended_Kalman_filter
Wikipedia, Ensemble Kalman Filter, https://en.wikipedia.org/wiki/Ensemble_Kalman_filter
Wikipedia, Lorenz system, https://en.wikipedia.org/wiki/Lorenz_system
Wikipedia, Mathematical optimization, https://en.wikipedia.org/wiki/Mathematical_optimization
Wikipedia, Nondimensionalization, https://en.wikipedia.org/wiki/Nondimensionalization
Wikipedia, Nelder–Mead method, https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
Wikipedia, Particle Swarm Optimization, https://en.wikipedia.org/wiki/Particle_swarm_optimization
Wikipedia, Quantile regression, https://en.wikipedia.org/wiki/Quantile_regression
Wikipedia, Simulated annealing, https://en.wikipedia.org/wiki/Simulated_annealing
Wikipedia, Tikhonov regularization, https://en.wikipedia.org/wiki/Tikhonov_regularization
Wikipedia, Tabu search, https://en.wikipedia.org/wiki/Tabu_search
Wikipedia, Unscented Kalman Filter, https://en.wikipedia.org/wiki/Unscented_Kalman_filter
Xiang Y., Sun D.Y., Fan W., Gong X.G., Generalized Simulated Annealing Algorithm and Its Application to the Thomson Model, Physics Letters A, 233, pp.216-220, 1997
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
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
Zupanski M., Maximum likelihood ensemble filter: Theoretical aspects, Monthly Weather Review, 133(6), pp.1710–1726, 2005