# PřF:FA045 Sel. chapt. mod. comp. meth. - Course Information

## FA045 Selected chapters from modern computational methods

**Faculty of Science**

Spring 2021

**Extent and Intensity**- 2/1/0. 4 credit(s). Type of Completion: zk (examination).
**Teacher(s)**- Mgr. Filip Hroch, Ph.D. (lecturer)

Mgr. Viktor Votruba, Ph.D. (lecturer) **Guaranteed by**- prof. Mgr. Jiří Krtička, Ph.D.

Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science

Contact Person: Mgr. Filip Hroch, Ph.D.

Supplier department: Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science **Prerequisites**(in Czech)- We are assuming some skills in mathematics: a common function handle (derivation, integration), linear algebra. Computer skills includes a basic knowledge of programming, and usual Unix tools.
**Course Enrolment Limitations**- The course is offered to students of any study field.
**Course objectives**(in Czech)- An introduction to modern computation methods of physical problems: non-linear partial diferential equations, massive data processing, etc.
**Learning outcomes**(in Czech)- Students will be able to applicate of the methods on solution of various numerical problems.
**Syllabus**(in Czech)- Explicit methods for partial diferential equations of hyperbolic and parabolic kinds by the finnite diference methods. Applications in fluid dynamics, Kelvin-Helmholtz unstability simulations.
- Implicit methods for for partial diferential equations of hyperbolic and parabolic kinds by the finnite diference methods. Applications on difusion, heat propagation. NUmerical limits of applications, stability.
- Smooth particle hydrodynamics (SPH), Particle Mesh(PM) and Particle in Cell (PIC). Plasma process simulation, two-component unstability.
- N-particle algorithms, particle in gravitation field, methods for molecular dynamics.
- Methods for handling of large data sets, data mining.
- Machine learning, supervised and unsupervised, Support Vector Machines, K-means clustering, neural networks.
- General classification, regression. Time-series anomaly recognizing.

**Literature**- BODENHEIMER, Peter.
*Numerical methods in astrophysics : an introduction*. New York: Taylor & Francis. 329 s. ISBN 9780750308830. 2007. info

- BODENHEIMER, Peter.
**Teaching methods**(in Czech)- A common form of teaching will be lectures iterative presenting given topics.
**Assessment methods**(in Czech)- A final project should be prepared, and referred, for the successful complete. Homeworks may be included.
**Language of instruction**- English
**Further comments (probably available only in Czech)**- The course is taught once in two years.

The course is taught: every week.

General note: L.

- Enrolment Statistics (recent)

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