 37 students
 18 lessons
 0 quizzes
 10 week duration
Course Title: Computational Physics
Programme: B Sc. Physics
Semester: VI
Course Introduction:
Computational physics is the study and implementation of numerical analysis to solve problems in physics for which a quantitative theory already exists. Historically, computational physics was the first application of modern computers in science, and is now a subset of computational science. Computational Physics can be considered as a subdiscipline of theoretical physics. It also establish the bridge between theoretical Physics and experimental physics.
Course Objective
This course covers various numerical methods for solving linear and nonlinear systems of equations, interpolation techniques, numerical differentiation and integrations. Algorithm for all the numerical methods are covered in the course.
Course Outcome:
After the successful completion of this course, student would be able to
 Solve systems of equations(linear, nonlinear and system of linear equation ) using any method which they learned. Students will become capable of selecting the optimal method for solving the system depending on the requirement.
 Perform interpolation using different techniques.
 Find the function(linear, nonlinear and exponential), which fit to the given set of data
 Perform numerical integration and differentiation using different techniques. Solve dynamics systems in physics using numerical techniques for which the analytical solution is difficult.
 Write algorithm for all the numerical techniques (outcomes: 1,2,3 and 4)
 Write computer programmes for numerical methods as they learn algorithms for each method.
Syllabus:
Module I (18 hours)
Solutions of Nonlinear Equations
Bisection Method – Newton Raphson method (two equation solution) – RegulaFalsi Method, Secant method – Fixed point iteration method – Rate of convergence and comparisons of these Methods
Solution of system of linear algebraic equations
Gauss elimination method with pivoting strategiesGaussJordan methodLU Factorization, Iterative methods (Jacobi method, GaussSeidel method)
Module II (18 hours)
Curve fitting: Regression and interpolation
Least squares Regression fitting a straight line, parabola, polynomial and exponential curve
Finite difference operatorsforward differences, divided difference; shift, average and differential operators Newton’s forward difference interpolation formulae Lagrange interpolation polynomial Newton’s divided difference interpolation polynomial
Module III (18 hours)
Numerical Differentiation and Integration
Numerical Differentiation formulae – Maxima and minima of a tabulated function Newton Cote general quadrature formula – Trapezoidal, Simpson’s 1/3, 3/8 rule.
Solution of ordinary differential equations
Taylor Series Method, Picard’s methodEuler’s and modified Euler’s method –Heun’s method Runge Kutta methods for 1st and 2nd order
(Algorithms for all numerical methods should be covered)

Module 1 A. Solution to Nonlinear Equations

Module 1 B. Solution of System of Linear Algebraic Equations

Module 2. Curve fitting: Regression and interpolation
 Least squares Regression fitting a straight line
 Fitting parabola, polynomial and exponential curve for a given data
 Finite difference operatorsforward differences, divided difference; shift, average and differential operators.
 Newton’s forward difference interpolation formula, Lagrange interpolation polynomial
 Newton’s divided difference interpolation polynomial

Module 3 A. Numerical Differentiation and Integration

Module 3 B. Solution of Ordinary Differential Equations
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