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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Mathematics for Mechanics

The course is not on the list Without time-table
Code Completion Credits Range Language
2013054 Z 4 3P+1C Czech
Lecturer:
Petr Sváček (guarantor)
Tutor:
Petr Sváček (guarantor)
Supervisor:
Department of Technical Mathematics
Synopsis:

Summary: Tensor calculus. Introduction to functional analysis. Calculus of variations.

• Orthogonal transformation of coordinate systems.

• Afinne orthogonal tensors and tensor operations.

• Tensor as linear operator and bilinear form.

• Metrics and metric spaces. Convergence. Completness.

• Linear normed space. Banach space.

• Linear space with scalar product (unitary space). Hilbert space.

• Contractive operators and Banach fixed point theorem.

• Function spaces in examples.

• Operators and functionals. Linear, continuous and bounded operator/functional.

• Derivative of a functional in the given direction. Gateaux differential and derivative.

• Necessary and sufficient conditions for extremes of a functional.

• Convex set and convex functional. Minimum of convex functional.

• Extremes of functional of different types. Euler equation. Necessary and sufficient conditions for extrema.

• Discrete methods for approximation of the minima of an functional. Ritz method.

Requirements:
Syllabus of lectures:

Summary: Tensor calculus. Introduction to functional analysis. Calculus of variations.

• Orthogonal transformation of coordinate systems.

• Afinne orthogonal tensors and tensor operations.

• Tensor as linear operator and bilinear form.

• Metrics and metric spaces. Convergence. Completness.

• Linear normed space. Banach space.

• Linear space with scalar product (unitary space). Hilbert space.

• Contractive operators and Banach fixed point theorem.

• Function spaces in examples.

• Operators and functionals. Linear, continuous and bounded operator/functional.

• Derivative of a functional in the given direction. Gateaux differential and derivative.

• Necessary and sufficient conditions for extremes of a functional.

• Convex set and convex functional. Minimum of convex functional.

• Extremes of functional of different types. Euler equation. Necessary and sufficient conditions for extrema.

• Discrete methods for approximation of the minima of an functional. Ritz method.

Syllabus of tutorials:

Summary: Tensor calculus. Introduction to functional analysis. Calculus of variations.

• Orthogonal transformation of coordinate systems.

• Afinne orthogonal tensors and tensor operations.

• Tensor as linear operator and bilinear form.

• Metrics and metric spaces. Convergence. Completness.

• Linear normed space. Banach space.

• Linear space with scalar product (unitary space). Hilbert space.

• Contractive operators and Banach fixed point theorem.

• Function spaces in examples.

• Operators and functionals. Linear, continuous and bounded operator/functional.

• Derivative of a functional in the given direction. Gateaux differential and derivative.

• Necessary and sufficient conditions for extremes of a functional.

• Convex set and convex functional. Minimum of convex functional.

• Extremes of functional of different types. Euler equation. Necessary and sufficient conditions for extrema.

• Discrete methods for approximation of the minima of an functional. Ritz method.

Study Objective:

Summary: Tensor calculus. Introduction to functional analysis. Calculus of variations.

• Orthogonal transformation of coordinate systems.

• Afinne orthogonal tensors and tensor operations.

• Tensor as linear operator and bilinear form.

• Metrics and metric spaces. Convergence. Completness.

• Linear normed space. Banach space.

• Linear space with scalar product (unitary space). Hilbert space.

• Contractive operators and Banach fixed point theorem.

• Function spaces in examples.

• Operators and functionals. Linear, continuous and bounded operator/functional.

• Derivative of a functional in the given direction. Gateaux differential and derivative.

• Necessary and sufficient conditions for extremes of a functional.

• Convex set and convex functional. Minimum of convex functional.

• Extremes of functional of different types. Euler equation. Necessary and sufficient conditions for extrema.

• Discrete methods for approximation of the minima of an functional. Ritz method.

Study materials:
Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2020-01-21
For updated information see http://bilakniha.cvut.cz/en/predmet1895506.html