2 edition of **Numerical solutions of P. D. E. with singular coefficients.** found in the catalog.

Numerical solutions of P. D. E. with singular coefficients.

Neoptolemos Cleopa

- 267 Want to read
- 13 Currently reading

Published
**1971** .

Written in English

- Differential equations, Partial -- Numerical solutions.

Classifications | |
---|---|

LC Classifications | QA374 .C55 |

The Physical Object | |

Pagination | iii, 99 l. |

Number of Pages | 99 |

ID Numbers | |

Open Library | OL5327837M |

LC Control Number | 72180778 |

Google Scholar [18] Kantorovich, L. These arise because a finite time is required to sense information and then react to it. Grundlehren Math. The differential-difference equation plays an important role in the mathematical modeling of various practical phenomena in the biosciences and control theory.

Contents: Partial differential equations and numerical methods; Linear algebra; Stability in the numerical solution of differential equations; etc. Solving eigenvalue or singular value problems[ edit ] Several important problems can be phrased in terms of eigenvalue decompositions or singular value decompositions. In the SFBIM, the solution is approximated by the leading terms of the asymptotic expansion 1 for the 2-D case, or the asymptotic expansion 2 for the 3-D case. Next, consider the value of the function derivative at some discrete pointsand let it be expressed as a linearly weighted some of the function values.

Hasanov Fundamental solutions of generalized bi-axially symmetric Helmholtz equation Complex Variables and Elliptic Equations 52 8 doi Carleman Estimates and null controllability of coupled degenerate systems. The DQM approximates the derivative with respect to a coordinate direction at a grid point by a weighted linear sum of all the functional values in that direction. For instance, the spectral image compression algorithm [3] is based on the singular value decomposition.

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Geometry and boundary conditions of the 2-D Laplacian model problem. In Solid Mechanics [ 16 ], function u represents the first stress invariant i.

CrossRef Google Scholar Copyright information. Theory Differ. Google Scholar [8] G. The interest in such a problem is motivated by the need to compute generalized stress intensity functions for the V-notched solids loaded by static loads.

The program is build progressively to make it multipurpose and easy to modify to solve any system of partial differential equations. The key to DQM is the determination of weighting coefficients for any order derivative discretization.

Let be the function values specified in a finite set of discrete points of the field domain. Fryant Growth and complete sequences of generalized bi-axially symmetric potentials Jour. This can be done by a finite element methoda finite difference method, or particularly in engineering a finite volume method.

The DQM is a simple and efficient numerical technique, which approximates the derivative with respect to a coordinate direction at a grid point by a weighted linear sum of all the functional values in that direction. Noordhoff, Groningen, The Netherlands Hasanov and E. These equations arise from the formulation of the mixed boundary value problems in applied physics and engineering.

They are also known as generalized stress intensity factors GSIFs. In[17], M. Variational inequality for the Stokes equations with constraint. Main articles: List of numerical analysis software and Comparison of numerical analysis software Since the late twentieth century, most algorithms are implemented in a variety of programming languages.

The method has been tested on standard elliptic problems exhibiting exponential convergence and Numerical solutions of P. D. E. with singular coefficients. book accuracy with respect to the number of singular functions.

Hardy-Sobolev type inequality and supercritical extremal problem. This problem is a version of a more general problem which was first suggested by Yosibash et al. The generalized approach uses two sets of base polynomials to determine the weighting coefficients[6].

Abramowitz, I. Google Scholar [9] J. Equation 1 the quadrature rule for a derivative is the essential basis of the Differential Quadrature Method.This paper proposes power series method (PSM) in order to find solutions for singular partial differential-algebraic equations (SPDAEs).

We will solve three examples to show that PSM method can be used to search for analytical solutions of SPDAEs. What is more, we will see that, in some cases, Padé posttreatment, besides enlarging the domain of convergence, may be employed in order to get the Cited by: 4.

In[16], they described a numerical approach based on finite difference method to solve a mathematical model arising from a model of neuronal variability.

In[14], M.K. Kadalbajoo and D. Kumar presented a numerical method based on fitted mess and B-spline technique for singularly perturbed differential-difference equations with small delay.

The singular boundary method (SBM) [22] was proposed by the authors in and is a strong-form boundary discretization technique. In order to regularize the singularities of fundamental solutions upon the coincidence of source and collocation points, the concept of the source intensity factors (SIFs) was first introduced, which is also called the origin intensity factors (OIFs) in some.Numerical Methods for Fractional Order Singular Partial Differential Equations with Variable Coefficients Article (PDF Available) in Mathematical Problems in Engineering (11) · March We establish Hardy - Download pdf and Carleman estimates for non-smooth degenerate/singular parabolic operators in divergence form with Neumann boundary conditions.

The degeneracy and the singularity occur both in the interior of the spatial domain. We apply these inequalities to deduce well-posedness and null controllability for the associated evolution problem.p>Author: Genni Fragnelli, Dimitri Mugnai.A Theoretical Introduction to Numerical Analysis presents ebook general methodology and principles of numerical analysis, illustrating these concepts using numerical methods from real analysis, linear algebra, and differential equations.

The book focuses on how to efficiently represent mathematical models for computer-based study.