Eigen value decomposition and numerical solution of differential equations are widely used in not only mechanical engineering but also most of science and engineering research fields. Because of a benefit of innovation of technology, many generalized software and simulators are available, but it is required to understand operating principle of mathematics and physics.
This course focuses on mathematical principle for programming which contains applied linear algebra, optimization method and numerical solution of differential equations.
By the end of this course, students will be able to;
(a) Understand Eigen value and Eigen vector of a matrix,
(b) Calculate inverse/pseudo-inverse of a large scale matrix.
(c) Understand the solution of the least square and optimization.
(d) Learn numerical solution of differential equation and it stability.
(e) Learn programming skill applying the above methods.
Inverse problem, Eigen value decomposition, Singular value decomposition, Least square method, Differential equation
Intercultural skills | Communication skills | Specialist skills | Critical thinking skills | Practical and/or problem-solving skills |
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- | - | ✔ | - | ✔ |
This course is organized by lecture and exercise.
Course schedule | Required learning | |
---|---|---|
Class 1 | Vector, Space, Matrix and Projection | Acquire a concept of n-dimensional space. Projection is represented by a matrix |
Class 2 | Positive and negative of matrix, null space | Understand positive/negative of matrix and null space |
Class 3 | Norm of vector and matrix (eigenvalue and power of matrix) | Understand norm, eigenvalue decomposition |
Class 4 | Solution of inverse problem (inverse matrix, singular value decomposition) | Understand inverse/pseudo-inverse matrix and singular value decomposition |
Class 5 | Optimization method | Aquire optimizatioin algorithm |
Class 6 | Least square method | Understand least square method |
Class 7 | Numerical solution of differential equations | Aqurie numerical solution of differential equation |
Class 8 | Difference approximation of derived function | Understand difference approximation of derived function |
None(Some handouts will be distributed)
Masatake Mori, 'numerical analysis', Kyoritsu Shuppan Co., Ltd.
Tetsuro Yamamoto, 'Introductory 'numerical analysis', Saiensu-sha Co., Ltd. Publishers
Exercise(30%) and final exam(70%)
Fundamentals of Numerical Analysis