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Relevant bibliographies by topics / Weak and strong numerical congergence

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Journal articles

Academic literature on the topic 'Weak and strong numerical congergence'

Author: Grafiati

Published: 9 September 2022

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Journal articles on the topic "Weak and strong numerical congergence"

1

Mosler, J. "Numerical analyses of discontinuous material bifurcation: strong and weak discontinuities." Computer Methods in Applied Mechanics and Engineering 194, no. 9-11 (2005): 979–1000. http://dx.doi.org/10.1016/j.cma.2004.06.018.

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2

Mavroyannis, Constantine. "A laser-excited three-level atom. II numerical results." Canadian Journal of Physics 68, no. 4-5 (1990): 411–21. http://dx.doi.org/10.1139/p90-065.

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Numerical calculations are presented for the interference spectra of a laser-excited three-level atom, where the strong and the weak atomic transitions are driven by resonant and nonresonant laser fields, respectively. The spectral functions describing the interference spectra for the electric dipole allowed excited state have been considered in the low- and high-intensity limit of the laser field operating in the strong transition. The interference spectra arise from the competition between short-lifetime spontaneous processes and short- and long-lifetime excitations induced by the strong and

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3

Mužík, Juraj. "Numerical Simulation of the Couette Flow Using Meshless Weak-strong Method." Procedia Engineering 91 (2014): 334–39. http://dx.doi.org/10.1016/j.proeng.2014.12.070.

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4

Bunch, James R. "The weak and strong stability of algorithms in numerical linear algebra." Linear Algebra and its Applications 88-89 (April 1987): 49–66. http://dx.doi.org/10.1016/0024-3795(87)90102-9.

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5

Englert, Roman, and Jörg Muschiol. "Numerical Evidence That the Power of Artificial Neural Networks Limits Strong AI." Advances in Artificial Intelligence and Machine Learning 02, no. 02 (2022): 338–46. http://dx.doi.org/10.54364/aaiml.2022.1122.

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A famous definition of AI is based on the terms weak and strong AI from McCarthy. An open question is the characterization of these terms, i.e., the transition from weak to strong. Nearly no research results are known for this complex and important question. In this paper we investigate how the size and structure of a Neural Network (NN) limits the learnability of a training sample, and thus, can be used to discriminate weak and strong AI (domains). Furthermore, the size of the training sample is a primary parameter for the training effort estimation with the big O function. The needed trainin

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6

Hilke, Michael, Mathieu Massicotte, Eric Whiteway, and Victor Yu. "Weak Localization in Graphene: Theory, Simulations, and Experiments." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/737296.

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We provide a comprehensive picture of magnetotransport in graphene monolayers in the limit of nonquantizing magnetic fields. We discuss the effects of two-carrier transport, weak localization, weak antilocalization, and strong localization for graphene devices of various mobilities, through theory, experiments, and numerical simulations. In particular, we observe a minimum in the weak localization and strong localization length reminiscent of the minimum in the conductivity, which allows us to make the connection between weak and strong localization. This provides a unified framework for both

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7

Kerman, R. A. "Strong and Weak Weighted Convergence of Jacobi Series." Journal of Approximation Theory 88, no. 1 (1997): 1–27. http://dx.doi.org/10.1006/jath.1996.3005.

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8

Ngodock, Hans, Matthew Carrier, Scott Smith, and Innocent Souopgui. "Weak and Strong Constraints Variational Data Assimilation with the NCOM-4DVAR in the Agulhas Region Using the Representer Method." Monthly Weather Review 145, no. 5 (2017): 1755–64. http://dx.doi.org/10.1175/mwr-d-16-0264.1.

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Abstract The difference between the strong and weak constraints four-dimensional variational (4DVAR) analyses is examined using the representer method formulation, which expresses the analysis as the sum of a first guess and a finite linear combination of representer functions. The latter are computed analytically for a single observation under both strong and weak constraints assumptions. Even though the strong constraints representer coefficients are different from their weak constraints counterparts, that difference is unable to help the strong constraints compensate for the loss of informa

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9

Abbasian Arani, Ali Akbar, and Majid Dehghani. "Numerical Comparison of Two and Three Dimensional Flow Regimes in Porous Media." Defect and Diffusion Forum 312-315 (April 2011): 427–32. http://dx.doi.org/10.4028/www.scientific.net/ddf.312-315.427.

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The purpose of this work is to study the fluid flow regimes in a porous media with high enough velocities (in the range of laminar flow). In our study, the results obtained from expanding Darcy’s equation to Forchheimer’s equation with volume averaging method have been used for studdying the fluid flow behavior in 2D and 3D models. Results of numerical simulations show that in all cases, there are weak inertial regime, strong inertial regime and transition zone. In all the cases, the domain of weak inertial regime is relatively narrow, and this problem is intensified in the 3D numerical simula

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10

Siopacha, Maria, and Josef Teichmann. "Weak and strong Taylor methods for numerical solutions of stochastic differential equations." Quantitative Finance 11, no. 4 (2011): 517–28. http://dx.doi.org/10.1080/14697680903493573.

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