Academic literature on the topic 'Numerical methods'

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Journal articles on the topic "Numerical methods"

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Murakami, Akira, Akihiko Wakai, and Kazunori Fujisawa. "Numerical Methods." Soils and Foundations 50, no. 6 (December 2010): 877–92. http://dx.doi.org/10.3208/sandf.50.877.

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Rodi, W. "Numerical methods." Journal of Wind Engineering and Industrial Aerodynamics 69-71 (July 1997): 131–32. http://dx.doi.org/10.1016/s0167-6105(97)00227-4.

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Eslami, M., and A. Neyrame. "Numerical methods." Computational Mathematics and Modeling 22, no. 1 (January 2011): 92–97. http://dx.doi.org/10.1007/s10598-011-9091-0.

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Ramos, J. I. "Numerical Methods." Applied Mathematical Modelling 14, no. 8 (August 1990): 444. http://dx.doi.org/10.1016/0307-904x(90)90101-a.

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Bultheel, A. "Numerical methods." Journal of Computational and Applied Mathematics 24, no. 3 (December 1988): N2. http://dx.doi.org/10.1016/0377-0427(88)90305-6.

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Fulda, Joseph S. "Numerical methods disguised." ACM SIGNUM Newsletter 21, no. 3 (July 1986): 31–32. http://dx.doi.org/10.1145/1057958.1057965.

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Eichner, Don W. "Numerical control methods." Computers & Industrial Engineering 15, no. 1-4 (January 1988): 184–86. http://dx.doi.org/10.1016/0360-8352(88)90083-6.

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Verschelde, J., and D. Roose. "Numerical Continuation Methods." Journal of Computational and Applied Mathematics 34, no. 2 (April 1991): N2—N3. http://dx.doi.org/10.1016/0377-0427(91)90050-t.

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Dawes, A. S. "Invariant numerical methods." International Journal for Numerical Methods in Fluids 56, no. 8 (2008): 1185–91. http://dx.doi.org/10.1002/fld.1749.

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Coutinho, A. L. G. A., L. P. Franca, and F. Valentin. "Numerical multiscale methods." International Journal for Numerical Methods in Fluids 70, no. 4 (November 22, 2011): 403–19. http://dx.doi.org/10.1002/fld.2727.

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Dissertations / Theses on the topic "Numerical methods"

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Kleditzsch, Stefan, and Birgit Awiszus. "Modeling of Cylindrical Flow Forming Processes with Numerical and Elementary Methods." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-97124.

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With flow forming – an incremental forming process – the final geometry of a component is achieved by a multitude of minor sequential forming steps. Due to this incremental characteristic associated with the variable application of the tools and kinematic shape forming, it is mainly suitable for small and medium quantities. For the extensive use of the process it is necessary to have appropriate simulation tools. While the Finite-Element-Analysis (FEA) is an acknowledged simulation tool for the modeling and optimization of forming technology, the use of FEA for the incremental forming processes is associated with very long computation times. For this reason a simulation method called FloSim, based on the upper bound method, was developed for cylindrical flow forming processes at the Chair of Virtual Production Engineering, which allows the simulation of the process within a few minutes. This method was improved by the work presented with the possibility of geometry computation during the process.
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Munro, Peter Robert Thomas. "Application of numerical methods to high numerical aperture imaging." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427816.

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Hilden, Sindre Kristensen. "Numerical Methods for Nonholonomic Mechanics." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9895.

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We discuss nonholonomic systems in general and numerical methods for solving them. Two different approaches for obtaining numerical methods are considered; discretization of the Lagrange-d'Alembert equations on the one hand, and using the discrete Lagrange-d'Alembert principle to obtain nonholonomic integrators on the other. Among methods using the first approach, we focus on the super partitioned additive Runge-Kutta (SPARK) methods. Among nonholonomic integrators, we focus on a reversible second order method by McLachlan and Perlmutter. Through several numerical experiments the methods we present are compared by considering error-growth, conservation of energy, geometric properties of the solution and how well the constraints are satisfied. Of special interest is the comparison of the 2-stage SPARK Lobatto IIIA-B method and the nonholonomic integrator by McLachlan and Perlmutter, which both are reversible and of second order. We observe a clear connection between energy-conservation and the geometric properties of the numerical solution. To preserve energy in long-time integrations is seen to be important in order to get solutions with the correct qualitative properties. Our results indicate that the nonholonomic integrator by McLachlan and Perlmutter sometimes conserves energy better than the 2-stage SPARK Lobatto IIIA-B method. In a recent work by Jay, however, the same two methods are compared and are found to conserve energy equally well in long-time integrations.

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Johansson, Christer. "Numerical methods for waveguide modeling /." Stockholm : Numerical Analysis and Computing Science (NADA), Stockholm university, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-992.

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Knight, Katherine. "Numerical methods for vortical flows." Thesis, Cranfield University, 2007. http://hdl.handle.net/1826/4535.

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An investigation into the current methods employed to conserve vorticity in numerical calculations is undertaken. Osher’s flux for the artificial compressibility equations is derived, implemented and validated in Cranfield University’s second order finite volume compressible flow solver MERLIN. Characteristic Decomposition is applied as a method of vorticity conservation in both the compressible and artificial compressibility MERLIN solvers. The performance of this method for vorticity conservation in both these solvers is assessed. Following a discussion of the issues associated with application of limiter functions on unstructured grids three modified versions of the method of Characteristic Decomposition are proposed and tested in both the compressible and incompressible solvers. It is concluded that the method of Characteristic Decomposition is an effective method for improving vorticity conservation and compares favourably in terms of increased computational cost to vorticity conservation through grid refinement.
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Hall, Stuart James. "Numerical methods and Riemannian geometry." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538692.

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Ashi, Hala. "Numerical methods for stiff systems." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/10663/.

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Some real-world applications involve situations where different physical phenomena acting on very different time scales occur simultaneously. The partial differential equations (PDEs) governing such situations are categorized as "stiff" PDEs. Stiffness is a challenging property of differential equations (DEs) that prevents conventional explicit numerical integrators from handling a problem efficiently. For such cases, stability (rather than accuracy) requirements dictate the choice of time step size to be very small. Considerable effort in coping with stiffness has gone into developing time-discretization methods to overcome many of the constraints of the conventional methods. Recently, there has been a renewed interest in exponential integrators that have emerged as a viable alternative for dealing effectively with stiffness of DEs. Our attention has been focused on the explicit Exponential Time Differencing (ETD) integrators that are designed to solve stiff semi-linear problems. Semi-linear PDEs can be split into a linear part, which contains the stiffest part of the dynamics of the problem, and a nonlinear part, which varies more slowly than the linear part. The ETD methods solve the linear part exactly, and then explicitly approximate the remaining part by polynomial approximations. The first aspect of this project involves an analytical examination of the methods' stability properties in order to present the advantage of these methods in overcoming the stability constraints. Furthermore, we discuss the numerical difficulties in approximating the ETD coefficients, which are functions of the linear term of the PDE. We address ourselves to describing various algorithms for approximating the coefficients, analyze their performance and their computational cost, and weigh their advantages for an efficient implementation of the ETD methods. The second aspect is to perform a variety of numerical experiments to evaluate the usefulness of the ETD methods, compared to other competing stiff integrators, for integrating real application problems. The problems considered include the Kuramoto-Sivashinsky equation, the nonlinear Schrödinger equation and the nonlinear Thin Film equation, all in one space dimension. The main properties tested are accuracy, start-up overhead cost and overall computation cost, since these parameters play key roles in the overall efficiency of the methods.
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Handschuh, Stefan. "Numerical methods in Tensor Networks." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-159672.

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In many applications that deal with high dimensional data, it is important to not store the high dimensional object itself, but its representation in a data sparse way. This aims to reduce the storage and computational complexity. There is a general scheme for representing tensors with the help of sums of elementary tensors, where the summation structure is defined by a graph/network. This scheme allows to generalize commonly used approaches in representing a large amount of numerical data (that can be interpreted as a high dimensional object) using sums of elementary tensors. The classification does not only distinguish between elementary tensors and non-elementary tensors, but also describes the number of terms that is needed to represent an object of the tensor space. This classification is referred to as tensor network (format). This work uses the tensor network based approach and describes non-linear block Gauss-Seidel methods (ALS and DMRG) in the context of the general tensor network framework. Another contribution of the thesis is the general conversion of different tensor formats. We are able to efficiently change the underlying graph topology of a given tensor representation while using the similarities (if present) of both the original and the desired structure. This is an important feature in case only minor structural changes are required. In all approximation cases involving iterative methods, it is crucial to find and use a proper initial guess. For linear iteration schemes, a good initial guess helps to decrease the number of iteration steps that are needed to reach a certain accuracy, but it does not change the approximation result. For non-linear iteration schemes, the approximation result may depend on the initial guess. This work introduces a method to successively create an initial guess that improves some approximation results. This algorithm is based on successive rank 1 increments for the r-term format. There are still open questions about how to find the optimal tensor format for a given general problem (e.g. storage, operations, etc.). For instance in the case where a physical background is given, it might be efficient to use this knowledge to create a good network structure. There is however, no guarantee that a better (with respect to the problem) representation structure does not exist.
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Möller, Ole. "Numerical methods for gravitational lensing." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620929.

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Larsson, Johan, and Isak Ågren. "Numerical Methods for Spectral Clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275701.

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The Aviation industry is important to the European economy and development, therefore a study of the sensitivity of the European flight network is interesting. If clusters exist within the network, that could indicate possible vulnerabilities or bottlenecks, since that would represent a group of airports poorly connected to other parts of the network. In this paper a cluster analysis using spectral clustering is performed with flight data from 34 different European countries. The report also looks at how to implement the spectral clustering algorithm for large data sets. After performing the spectral clustering it appears as if the European flight network is not clustered, and thus does not appear to be sensitive.
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Books on the topic "Numerical methods"

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Karniadakis, George Em, ed. Numerical Methods. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110571684.

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Iyengar, S. R. K. Numerical methods. New Delhi: New Age International (P) Ltd., Publishers, 2009.

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Shevchenko, Alesya. Numerical methods. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/996207.

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The textbook describes the basics of numerical methods for solving problems of mathematical analysis, linear algebra and ordinary differential equations. Considerable attention is paid to the issues of algorithmization of methods. It can be used when performing laboratory, course, final qualification and research works. Each topic contains a theoretical justification and a large number of examples of solving practical problems using the Maple mathematical package. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students, postgraduates, university teachers, as well as for engineers and researchers who use numerical methods to solve applied problems.
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Tanguy, Jean-Michel, ed. Numerical Methods. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9781118557877.

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Faires, J. Douglas. Numerical methods. Boston: PWS-Kent Pub. Co., 1993.

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1934-, Björck Åke, and Björck Åke 1934-, eds. Numerical methods. Mineola, N.Y: Dover Publications, 2003.

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L, Burden Richard, ed. Numerical methods. 2nd ed. Pacific Grove, CA: Brooks/Cole Pub. Co., 1998.

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Dukkipati, Rao V. Numerical methods. New Delhi: New Age International Ltd., 2010.

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Colloquium on Numerical Methods (4th 1986 Miskolc, Hungary). Numerical methods. Amsterdam: North-Holland, 1988.

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Donald, Greenspan, and Rózsa P, eds. Numerical methods. Amsterdam: North-Holland Pub. Co., 1991.

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Book chapters on the topic "Numerical methods"

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Crighton, D. G., A. P. Dowling, J. E. Ffowcs Williams, M. Heckl, and F. G. Leppington. "Numerical Methods." In Modern Methods in Analytical Acoustics, 283–310. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-0399-8_10.

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Courant, Richard, and Fritz John. "Numerical Methods." In Introduction to Calculus and Analysis, 481–509. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58604-0_6.

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Lehner, Günther. "Numerical Methods." In Electromagnetic Field Theory for Engineers and Physicists, 506–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76306-2_8.

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Sanz-Serna, J. M., and M. P. Calvo. "Numerical methods." In Numerical Hamiltonian Problems, 25–39. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3093-4_3.

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Cercignani, Carlo, and David H. Sattinger. "Numerical Methods." In Scaling Limits and Models in Physical Processes, 175–85. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8810-3_10.

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Alobaid, F., B. Epple, R. Leithner, H. Müller, H. Zindler, K. Ponweiser, and H. Walter. "Numerical Methods." In Numerical Simulation of Power Plants and Firing Systems, 161–320. Vienna: Springer Vienna, 2017. http://dx.doi.org/10.1007/978-3-7091-4855-6_3.

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Platen, Eckhard, and David Heath. "Numerical Methods." In A Benchmark Approach to Quantitative Finance, 551–613. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-47856-0_15.

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Bona, Carles, Carles Bona-Casas, and Carlos Palenzuela-Luque. "Numerical Methods." In Elements of Numerical Relativity and Relativistic Hydrodynamics, 109–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01164-1_5.

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Eichfelder, Gabriele. "Numerical Methods." In Vector Optimization, 153–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54283-1_9.

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Kuehn, Christian. "Numerical Methods." In Applied Mathematical Sciences, 295–325. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12316-5_10.

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Conference papers on the topic "Numerical methods"

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Gale, J. D. "Modelling the thermal expansion of zeolites." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59485.

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Parlinski, Krzysztof. "Calculation of phonon dispersion curves by the direct method." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59457.

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Horbach, Jürgen, Walter Kob, and Kurt Binder. "The boson peak in amorphous silica: Results from molecular dynamics computer simulations." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59458.

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Paciaroni, Alessandro, Anna Rita Bizzarri, and Salvatore Cannistraro. "Molecular dynamics simulation of inelastic neutron scattering spectra of Copper Azurin hydration water." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59459.

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Li, Jichen, and John Tomkinson. "Simulation of inelastic neutron scattering spectra for water ice - A most effective way of testing water potentials." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59460.

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Elliott, S. R., A. Haar, R. D. Oeffner, and S. N. Taraskin. "Extracting the vibrational density of states from neutron scattering data: beyond the effective density of states." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59461.

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Hanham, Matthew L., and Robert F. Pettifer. "EXAFS calculations using Debye-Waller factors deduced from inelastic neutron scattering." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59462.

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Navarro, A., M. Fernández-Gómez, J. J. López-González, F. Partal, J. Tomkinson, and G. Kearley. "Density functional theory and ab initio methods applied to the analysis of inelastic neutron scattering spectra." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59463.

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Leulliot, Nicolas, Hervé Jobic, and Mahmoud Ghomi. "Search for a reliable nucleic acid force field using neutron inelastic scattering and quantum mechanical calculations: Bases, nucleosides and nucleotides." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59464.

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Ulicny, Jozef, Nicolas Leulliot, Lydie Grajcar, Marie-Hélène Baron, Hervé Jobic, and Mahmoud Ghomi. "NIS, IR and Raman spectra with quantum mechanical calculations for analyzing the force field of hypericin model compounds." In Neutrons and numerical methods. AIP, 1999. http://dx.doi.org/10.1063/1.59465.

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Reports on the topic "Numerical methods"

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Giraldo, Francis X. Advanced Numerical Methods for Numerical Weather Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada609966.

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Giraldo, Francis X. Advanced Numerical Methods for Numerical Weather Prediction. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada625715.

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Schoonover, Joseph A. Introduction to Numerical Methods. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1312632.

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Giraldo, Francis X. Advanced Numerical Methods for Numerical Weather Prediction (NWP). Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada627348.

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Tewarson, Reginald P. Numerical Methods for Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada177283.

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Dupuis, Paul, and Harold J. Kushner. Numerical Methods in Stochastic Control. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada313649.

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Tewarson, Reginald P. Numerical Methods for Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada162722.

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Skeel, R. D. Numerical methods for molecular dynamics. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/5436878.

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Rozovskii, Boris, and Alexander Tartakovsky. Nonlinear Filtering: Analysis and Numerical Methods. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada399200.

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Russell, Thomas F. Numerical Dispersion in Eulerian-Lagrangian Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada445724.

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