phy611 - Theoretical Methods (Vollständige Modulbeschreibung)
Modulbezeichnung | Theoretical Methods | |
Modulkürzel | phy611 | |
Kreditpunkte | 6.0 KP | |
Workload | 180 h
( 180 h )
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Einrichtungsverzeichnis | Institut für Physik | |
Verwendbarkeit des Moduls |
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Zuständige Personen |
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Teilnahmevoraussetzungen | basic programming skills (matlab, python, C/C++) |
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Kompetenzziele | The goal of this module is to extend the training in theoretical methods for engineering physics through the acquisition of solid and in-depth knowledge of advanced concepts and through their practice with computer simulations. Depending on the chosen course, the students will have the opportunity to strengthen their knowledge in quantum material modelling (Density-functional theory), signal processing, fluid dynamics (Modelling and Simulation), computational physics, and machine learning. In this way, they will develop skills to relate the conceptual design of models, their numerical implementation, and the physical analysis of the produced data, with the results of field and/or laboratory measurements. |
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Modulinhalte | Computer Physics Debugging; data structures; algorithms; random numbers; data analysis; percolation; Monte Carlo simulations; finite-size scaling; quantum Monte Carlo; molecular dynamics simulations; event-driven simulations; graphs and algorithms; genetic algorithms; optimization problems.
Density-functional theory The many-body problem; the Hartree-Fock approximation; Homogeneous electron gas; Hohenberg-Kohn theorems; Kohn-Sham equations; exchange-correlation potentials; pseudopotentials; basis sets.
Machine learning Unsupervised learning methods; algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction; Relations to neural network models; learning in biological systems.
Modelling and Simulation Advanced fluid dynamics including 3D, transient and compressible processes; Theory of similarity, range of dimensionless numbers; Potential Theory; Numerical Algorithms and possibilities of independent coding of simplest mathematical models; Introduction of a complete chain of Open-Source-CFD-Tools; Contactless high-resolving measuring techniques in the fluid dynamics.
Signal processing System properties; Discrete-time signal processing; Statistical signal processing; Adaptive filters. |
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Literaturempfehlungen | Computer Physics - T. H. Cormen, S. Clifford, C.E. Leiserson, und R.L. Rivest: Introduction to Algorithms. MIT Press, 2001; - K. Hartmann: Practical guide to computer simulation. World- Scientific, 2009; - J. M. Thijssen: Computational Physics. Cambridge University Press, 2007; - M. Newman, G. T. Barkema: Monte Carlo Methods in Statistical Physics. Oxford University Press, 1999.
Density-functional theory - R. Martin, Electronic Structure, Cambridge University Press (2004); - F. Bechstedt, Many-body approach to electronic excitations, Springer (2015); - F. Giustino, Materials modelling using density functional theory, Oxford University Press (2014).
Machine learning - C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006; - D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge
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Unterrichtsprachen | Deutsch, Englisch | |
Dauer in Semestern | 1 Semester | |
Angebotsrhythmus Modul | halbjährlich | |
Aufnahmekapazität Modul | unbegrenzt | |
Modulart | Pflicht / Mandatory | |
Lehr-/Lernform | 1 Prüfung: – Klausuren zwischen 90 Min. und 180 Min., – Mündliche Prüfung zwischen 20 Min. und 45 Min., – Referat zwischen 10 Seiten und 20 Seiten schriftlicher Auseinandersetzung und zwischen 15 Min. und 30 Min. Vortrag, – Hausarbeit zwischen 15 und 30 Seiten |
Lehrveranstaltungsform | Kommentar | SWS | Angebotsrhythmus | Workload Präsenz |
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Vorlesung | 2 | SoSe oder WiSe | 28 | |
Übung | 2 | SoSe oder WiSe | 28 | |
Präsenzzeit Modul insgesamt | 56 h |
Prüfung | Prüfungszeiten | Prüfungsform |
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Gesamtmodul | Lectures (2 or 4 hours per week) / Exercises (1 or 2 hours per week) |