Objectives and competences
Students get basic theoretical and practical knowledge from the models in Physics
Student prepares one model in the scope of the laboratory work
Seminar work is designed for preparing theoretical summary of one model.
Content (Syllabus outline)
- Universal numerical model systems
- Graphical presentation of data: software tools
- Random walk: landscape and step rule, evolution models, applications in nature
- Cellular automata: modeling of self-organized critical behavior
- Non-linear systems: chaos, fractals, characterization
- Universal phenomenological models: description of the model, equilibrium conditions and equations, evaluation of measurable response functions, critical behavior
- Monte Carlo methods: simulations and data analysis
- Molecular dynamics: simulations and data analysis
- Phase transitions: analysis of critical behavior for a given case using a software tool
- Evolution programming: genetic algorithms
- Neural networks: learning rules, deep learning, convolutional neural networks, large language models
- Quantum computers: Shor algorithm, Grover's algorithm, BB84, Hadamard gate
Learning and teaching methods
Lecture, discussion, case studies, problem based learning, laboratory work with computers.
Intended learning outcomes - knowledge and understanding
• The student understands and applies various physical models.
• The student creates complex physical models.
• The student analyses physical models, solves them and evaluates the obtained results.
Intended learning outcomes - transferable/key skills and other attributes
• Work with the models is transferable to non-physical fields, for example to economy.
Readings
• F. J. Vesely, Computational Physics: An Introduction, Springer, 2012.
• Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016 (dostopno na https://www.deeplearningbook.org/ ).
• C. P. Williams, Explorations in Quantum Computing, Springer, 2010 (dostopno na https://link.springer.com/book/10.1007/978-1-84628-887-6 ).
• C. Bernhardt, Quantum Computing for Everyone, The MIT Press, 2019 (dostopno na https://direct.mit.edu/books/book/4186/Quantum-Computing-for-Everyone ).
Dodatna literatura / Additional Readings:
• P. Bak, How Nature Works: The Science of Self-Organized Criticality, Springer, 1996.
• M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, 1998.
• Novejši članki v Physical Review Letters, Nature, Science in drugih sorodnih revijah./ Recent articles in Physical Review Letters, Nature, Science and similar journals.
Prerequisits
None.
Recommended basic knowledge of classical physics, programming and mathematical physics.
Additional information on implementation and assessment coursework 35
seminar paper 35
oral exam 30
each individual part must be positively evaluated before the evaluation of the next part can begin
