By Jenna Brandenburg, Lashaun Clemmons

This publication offers a normal method of research of Numerical Differential Equations and Finite aspect strategy

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Forward, backward, and central differences Only three forms are commonly considered: forward, backward, and central differences. A forward difference is an expression of the form Depending on the application, the spacing h may be variable or constant. A backward difference uses the function values at x and x − h, instead of the values at x + h and x: Finally, the central difference is given by Relation with derivatives The derivative of a function f at a point x is defined by the limit If h has a fixed (non-zero) value, instead of approaching zero, then the right-hand side is Hence, the forward difference divided by h approximates the derivative when h is small.

By doing the above step, we have found the slope of the line that is tangent to the solution curve at the point (0,1). Recall that the slope is defined as the change in y divided by the change in t, or . The next step is to multiply the above value by the step size h. Since the step size is the change in t, when we multiply the step size and the slope of the tangent, we get a change in y value. This value is then added to the initial y value to obtain the next value to be used for computations.

Discrete Poisson equation In mathematics, the Discrete Poisson Equation is the finite difference analog of the Poisson equation. In it, the discrete Laplace operator takes the place of the Laplace operator. The discrete Poisson equation is frequently used in numerical analysis as a stand-in for the continuous Poisson equation, although it is also studied in its own right as a topic in discrete mathematics. On a two-dimensional rectangular grid Using the finite difference numerical method to discretize the 2 dimensional Poisson equation (assuming a uniform spatial discretization) on an m x n grid gives the following formula: where and .