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AHSB11.17 Evaluate the inverse Fourier transform, Fourier sine, and cosine transform of the given function. Note: The roots of an equation are the abscissa of the points where the graph. Since f 0 0 we conclude that the root lies between x and x0 3 The third approximation of the root is.

In the false position method, we will find the root of the equation f x 0 Consider two initial approximate values ​​x0 and x1 close to the required root, so that f x 0 and f x 1. We can see that the obtained points x x x2, ,3 4,… converge to the expected root of the equation. Therefore the root between x and x2 is 1 and the second order approximation of the root is.

Apply Newton – Raphson method to find an approximate root, correct to three decimal places, of the equation x3  3x 5 0, which lies near x2. A consequence of this restriction is that the Laplace transform of a function is a holomorphic function of the variable s.

Integration

Convolution theorem

Initial Value Theorem

Final Value Theorem

If we consider the statement y f x x  0  x xn, we understand that we can find the value of y corresponding to any value of x in the range x0 x xn. If the function f x  is simple and continuous and is known explicitly, the values ​​of f x  for certain values ​​of x such as x x0, ,..1 xn can be calculated. In this chapter we introduce what are called the forward, backward, and central differences of a function y f x.

These differences and the three standard examples of finite differences play a fundamental role in the study of differential calculus, which is an essential part of numerical applied mathematics. The first forward differences between the first forward differences are called the second forward differences and are denoted by 2y0,2y1.

Forward Difference Table:-

Backward Differences:-

The first backward differences of the first background differences are called second differences and are denoted by 2y2,2y r i.e.,.

Backward Difference Table:-

Central Differences:-

26 The first central differences from the first central differences are called the second central differences and are denoted by . Example: Given from the central . difference table and record the values ​​of by taking Sol. If it is a polynomial of degree n and the values ​​of x are equidistant from each other, then it is constant.

So the second difference of a polynomial of degree n is a polynomial of degree that continues as we get. Gauss's interpolation formula: - We take as one of the specified x which is around the center of the difference table and denote by and its corresponding value. Then the middle part of the forward difference table appears, as shown on the next page.

Using expressions (1) and (2), we now obtain two versions of the following Newton's forward interpolation formula. The constants can be determined by substituting one of the values ​​into the above equation.

Inversion from Basic Properties 1. Linearity

Shifting Ex

Scaling Ex

Integration Ex

Convolution Ex

Partial Fraction If F(s)

Differentiation with Respect to a Number Ex. 11

Method of Differential Equation Ex. 12

Ordinary Differential Equations with Constant Coefficients Ex. 1

  • POWER CURVE:-
  • EXPONENTIAL CURVE :-
  • Fit a second degree parabola to the following data
  • Fit a curve to the following data

Suppose a data is given in two variables x & y, the problem of finding an analytical expression of the form that fits the given data is called curve fitting. Solving these equations for a, b and substituting in (1), we obtain the required line of best fit to the given data. Let the equation of the parabola fit. The parabola (1) goes through the data points we have.

The sum of the squares of these errors is For E to be minimum, we have. From these equations the values ​​A and b can be calculated, then a = antilog (A) substituting a & b in (1) to obtain the required curve of best fit. By substituting the values ​​of a and b thus obtained in (1), we obtain the curve of best fir for the given data.

Using the method of least squares, find the straight line that best fits the following data. The Fourier transform named after Joseph Fourier is a mathematical transformation used to transform signals between time (or spatial) domain and frequency domain, which has many applications in physics and engineering. The term itself refers to both the transformation operation and to the function it produces.

In the case of a periodic function in time (such as a continuous but not necessarily sinusoidal musical sound), the Fourier transform can be simplified to the computation of a discrete set of complex amplitudes called Fourier coefficients. Furthermore, when a time-domain function is sampled for ease of storage or computer processing, it is still possible to recreate a version of the original Fourier transform according to the Poisson summation formula, also known as the discrete-time Fourier transform. Where k(s, x) is a known function called the kernel of the transformation, s is called the parameter of the transformation.

Using Taylor’s series method, solve the equation for given that when

89 Since h is not given, much better accuracy is obtained by breaking the interval (0,0.1) into five steps.

Fourth order R-K Formula

  • Apply the 4 th order R-K method to find an approximate value of y when x=1.2 in steps of 0.1,given that

5 Find the general solution of the first-order linear partial differential equation with the constant coefficients: 4ux+uy=x2y. 6 Find the general solution of the partial differential equation y2up + x2uq = y2x Sol The auxiliary system of equations is. Finally, we assume that the vibrations are quite minimal in relation to the total length of the string, i.e.

What kind of function do we want to solve for to keep track of string movement. The function u(x,t) then gives the vertical displacement of the string at any point, x, along the string, at any given time t. Because of our first assumption, there is only one force to keep track of in our situation, that of string tension.

The mass of the string is simple, just x, where  is the mass per unit length of the string, and. Now, finally, notice that tan is equal to the slope at the left end of the string segment, which is just. As x goes to 0, the left side of the equation is actually equal to.

This equation, which governs the motion of the vibrating string over time, is called the one-dimensional wave equation. Actually we would also need to know the initial velocity of the string, which is correct. In this fundamental mode the widest vibrational displacement occurs in the center of the string (see figures below).

The way these different modes are combined makes it possible to produce solutions to the wave equation with different initial shapes and initial velocities of the string. To ensure that the boundary conditions are met, we need (28) u(0,t)0andu(l,t)0 for all values ​​of t. The first boundary condition implies that. What we would like to do is find out what the temperature of the object is.

More precisely, it is the flux integral over the surface of E of the heat flow vector field F. One of the interesting things to note at this point is how similar this PDE looks to the wave equation PDE.

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