MLU
PHY.06614.03 - Advanced Computational Physics (Complete module description)
Original version English
PHY.06614.03 5 CP
Module label Advanced Computational Physics
Module code PHY.06614.03
Semester of first implementation
Faculty/Institute Institut für Physik
Module used in courses of study / semesters
  • Mathematik (MA120 LP) (Master) > Mathematik MathematikMA120, Version of accreditation valid from WS 2022/23 > Anwendungsfach Physik (20 LP sind zu erbringen)
  • Mathematik (MA120 LP) (Master) > Mathematik MathematikMA120, Version of accreditation (WS 2013/14 - SoSe 2023) > Anwendungsfach Physik
  • Medizinische Physik (MA120 LP) (Master) > Physik Medizinische PhysikMA120, Version of accreditation valid from WS 2019/20 >
  • Physik (MA120 LP) (Master) > Physik PhysikMA120, Version of accreditation valid from WS 2019/20 > Theoretische Physik
  • Physik und Digitale Technologien (180 LP) (Bachelor) > Physik Physik u. Dig. Tech. 180, Version of accreditation valid from WS 2019/20 > Wahlobligatorische Ergänzungsfächer
Responsible person for this module
Further responsible persons
Prof. Dr. Miguel Marques
Prerequisites
Skills to be acquired in this module
  • 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
Module contents
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
Forms of instruction Seminar (4 SWS)
Course
Languages of instruction German, English
Duration (semesters) 1 Semester Semester
Module frequency jedes Sommersemester
Module capacity unlimited
Time of examination
Credit points 5 CP
Share on module final degree Course 1: %; Course 2: %.
Share of module grade on the course of study's final grade 1
Module course label Course type Course title SWS Workload of compulsory attendance Workload of preparation / homework etc Workload of independent learning Workload (examination and preparation) Sum workload
Course 1 Seminar Projektseminar 4 0
Course 2 Course Selbststudium 0
Workload by module 150 150
Total module workload 150
Examination Exam prerequisites Type of examination
Course 1
Course 2
Final exam of module
mündl. Prüfung oder Klausur oder Seminarvortrag oder Hausarbeit
Exam repetition information
Prerequisites and conditions Prerequisites Frequency Compulsory attendance Share on module grade in percent
Course 1 Summer semester No %
Course 2 Summer semester No %