Introduction to Computational Physics 2

Garant předmětu:

Milan Kuchařík

Lecturer:

Pavel Eichler

Tutor:

Pavel Eichler

Supervisor:

Department of Software Engineering

Synopsis:

Nonlinear models, complex systems, chaotic systems, fractals and their applications in physics. Artificial intelligence methods: neural networks, machine learning, genetic algorithms, expert systems and their applications in physics. Quantum computing. Virtual reality.

Requirements:

Syllabus of lectures:

1.Complex systems, chaos

2.Chaotic systems in physics

3.Fractals, applications in physics

4.Neural networks, applications in physics

5.Machine learning, applications in physics

6.Genetic algorithms, applications in physics

7.Expert systems, applications in physics

8.Principle of quantum computers

9.Algorithms for quantum computers

10.Visualization by virtual reality

11.Presentations

Syllabus of tutorials:

following lecture

Study Objective:

Study materials:

Key references:

[1] R. H. Landau, M. J. Paez, Ch. C. Bordeianu: Computational Physics: Problem Solving with Python, 3rd Edition, Wiley, 2015.

[2] H. Gould, J. Tobochnik, W. Christian: An Introduction to Computer Simulation Methods: Applications to Physical Systems, CreateSpace Independent Publishing Platform; 3rd Revised edition, 2017.

Recommended references:

[3] P. Kim:MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress, 2017.

Study aids:

Computer classroom UNIX.

Note:

Time-table for winter semester 2023/2024:

Time-table is not available yet

Time-table for summer semester 2023/2024:

Time-table is not available yet

The course is a part of the following study plans:

• Applications of Informatics in Natural Sciences (compulsory course in the program)