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There are so many physical situations in applied mathematics and engineering that can be described by PDEs; these models possess the disadvantage of having many sources of uncertainties around their mathematical representation.

Moreover, to find the exact solutions of those uncertain PDEs is not a trivial task especially if the PDE is described in two or more dimensions. Given the continuous nature and the temporal evolution of these systems, differential neural networks are an attractive option as nonparametric identifiers capable of estimating a 3D distributed model.

To verify the qualitative behavior of the suggested methodology, here a nonparametric modeling problem for a distributed parameter plant is analyzed. Introduction 1. The dynamic description of natural phenomenons are usually described by a set of differential equations using mathematical modeling rules [1]. Almost every system described in PDE has already appeared in the one- and two-dimensional situations. Appending a third dimension ascends the dimensional ladder to its ultimate rung, in physical space at least.

For instance, linear second-order 3D partial differential equations appear in many problems modeling equilibrium configurations of solid bodies, the three-dimensional wave equation governing vibrations of solids, liquids, gases, and electromagnetic waves, and the three-dimensional heat equation modeling basic spatial diffusion processes.

Unfortunately, the most powerful of the planar tools, conformal mapping, does not carry over to higher dimensions. In this way, many numerical techniques solving such PDE, for example, the finite difference method FDM and the finite element method FEM , have been developed see [2, 3]. The principal disadvantage of these methods is that they require the complete mathematical knowledge of the system to define a mesh domain.



Samujas A comparison of results for methods i ii and iii for. Oxford University Press Amazon. Get fast, free shipping with Amazon Prime. Discover Prime Book Box for Kids.

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In this method, functions are represented by their values at certain grid points and derivatives are approximated through differences in these values. MOL allows standard, general-purpose methods and software, developed for the numerical integration of ordinary differential equations ODEs and differential algebraic equations DAEs , to be used. A large number of integration routines have been developed over the years in many different programming languages, and some have been published as open source resources. This leads to a system of ordinary differential equations to which a numerical method for initial value ordinary equations can be applied. The method of lines in this context dates back to at least the early s. It uses variational methods the calculus of variations to minimize an error function and produce a stable solution. Analogous to the idea that connecting many tiny straight lines can approximate a larger circle, FEM encompasses all the methods for connecting many simple element equations over many small subdomains, named finite elements, to approximate a more complex equation over a larger domain.


ISBN 13: 9780198596509

We assume that and has full rank. We present a numerical method to compute the solution for fully determined systems and compatible overdetermined systems. Our method reduces the original system to a smaller system of equations in alone. The iterative process to solve the smaller system only requires the LU factorization of one matrix per step, and the convergence is quadratic. Once has been obtained, is computed by direct solution of a linear system.


Numerical Solution of Ordinary Differential Equations


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