Physik und Digitale Technologien (180 LP) (Bachelor) > Physik Physik u. Dig. Tech. 180, Akkreditierungsfassung gültig ab WS 2019/20 > Wahlobligatorische Ergänzungsfächer
Modulverantwortliche/r
Weitere verantwortliche Personen
Prof. Dr. Miguel Marques
Teilnahmevoraussetzungen
Kompetenzziele
Learn to elaborate strategies to solve scientific problems using a computer
Learn some of the main algorithms and techniques used to solve problems in the different areas of Physics
Consolidate knowledge of programming and of algorithmic thinking
Deepen the knowledge in several areas of Physics by performing computer experiments
Modulinhalte
These are some of the subjects that may be taught in this course
Basis-set methods to solve partial differential equations. Finite-element method applied to classical problems with complex geometries, such as calculation of normal modes of vibration, propagation of heat, solution of Poisson%u2019s equation, etc.; Gaussian basis sets and plane-waves to solve the Schrödinger equation
Fourier transforms. Basic knowledge of the discrete and the fast Fourier transform methods; Analysis of sound-waves, including generation of wave-forms, filters, etc. Image analysis,filters, compression algorithms, etc.; Time-series analysis and the extraction of spectra; Compressed sensing and its applications to Physics
Monte-Carlo methods. Random number generation; Markov chains; Metropolis algorithm; kinetic Monte-Carlo; Variational and diffusion Monte-Carlo
Parallel programming. Parallel paradigms; Message-passing interface; Shared-memory systems; CPU vs GPU programming; CUDA
Machine learning; Supervised vs unsupervised learning; Algorithms (SVP, regression tress, neural networks, etc.); Deep learning; Reinforcement learning; Applications to physical problems
Lehrveranstaltungsformen
Seminar (4 SWS)
Kursus
Unterrichtsprachen
Deutsch, Englisch
Dauer in Semestern
1 Semester Semester
Angebotsrhythmus Modul
jedes Sommersemester
Aufnahmekapazität Modul
unbegrenzt
Prüfungsebene
Credit-Points
5 CP
Modulabschlussnote
LV1: %; LV2: %.
Faktor der Modulnote für die Endnote des Studiengangs
1
Modulveranstaltung
Lehrveranstaltungsform
Veranstaltungstitel
SWS
Workload Präsenz
Workload Vor- / Nachbereitung
Workload selbstgestaltete Arbeit
Workload Prüfung incl. Vorbereitung
Workload Summe
LV 1
Seminar
Projektseminar
4
0
LV 2
Kursus
Selbststudium
0
Workload modulbezogen
150
150
Workload Modul insgesamt
150
Prüfung
Prüfungsvorleistung
Prüfungsform
LV 1
LV 2
Gesamtmodul
mündl. Prüfung oder Klausur oder Seminarvortrag oder Hausarbeit