phy611 - Theoretical Methods

phy611 - Theoretical Methods

Institute of Physics 6 KP
Module components Semester courses Wintersemester 2022/2023 Examination
Lecture
  • No access 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke

    Wednesday: 12:15 - 13:45, weekly (from 19/10/22), Location: W03 1-156
    Dates on Wednesday, 22.02.2023 15:00 - 19:00, Location: W03 1-161
    The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches. This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate. The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups. The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields. Weblink: www.uni-oldenburg.de/ml
  • No access 5.04.4521 - Computerorientierte Physik Show lecturers
    • Prof. Dr. Alexander Hartmann

    Dates on Thursday, 27.10.2022, Thursday, 10.11.2022 14:15 - 15:45, Friday, 25.11.2022 16:15 - 19:45, Thursday, 08.12.2022 14:15 - 15:45, Wednesday, 21.12.2022 16:15 - 19:45, Friday, 20.01.2023 14:15 - 15:45, Monday, 20.02.2023 13:15 - 14:00, Monday, 20.02.2023 14:00 - 20:00, Tuesday, 21.02.2023 13:15 - 14:00, Tuesday, 21.02.2023 14:00 - 20:00, Wednesday, 22.02.2023 13:15 - 14:00 ...(more)
    Location: W02 1-143, W01 0-008 (Rechnerraum), W01 0-006
    Debugging, Datenstrukturen, Algorithmen, Zufallszahlen, Daten- analyse, Perkolation, Monte-Carlo-Simulationen, Finite-Size Scaling, Quanten-Monte-Carlo, Molekulardynamik-Simulationen, ereignisgetriebene Simulationen, Graphen und Algorithmen, genetische Algorithmen, Optimierungsprobleme
  • No access 5.04.4571 - Density-functional theory Show lecturers
    • Prof. Dr. Caterina Cocchi

    Wednesday: 14:15 - 15:45, weekly (from 19/10/22)
    Friday: 08:15 - 09:45, weekly (from 21/10/22)
    Description: The objective of this class is to introduce students to ab initio methods for electronic-structure calculations based on density-functional theory (DFT). The topics will be approached from the viewpoint of condensed-matter physics. In the first part of the semester, theoretical lectures will be accompanied by exercise sessions. The last few weeks of the term will be exclusively dedicated to hand-on tutorials. At the end of this course, participants are expected to be familiar with the theoretical foundation of DFT, to be able to perform a DFT calculation with good control of the given approximations, and to know how to interpret the outcoming results. The course as a whole (theoretical lectures, exercises, and hands-on tutorials) can be offered in presence, in digital form, or even in a mixed regime, depending on the circumstances and on the students’ needs. The course is addressed to Master’s students in theoretical physics. However, Master’s students in experimental physics, doctoral students in all specializations, and Bachelor’s students who are interested in (computational) electronic-structure theory are very welcome to participate. Good knowledge of quantum mechanics is the only prerequisite to attend this class.
  • No access 5.04.4665 - Modelling and Simulation Show lecturers
    • Jann Strybny
    • Arne Daniel

    Monday: 08:45 - 12:00, weekly (from 17/10/22)
    Contact: jann.strybny@hs-emden-leer.de arne.daniel@hs-emden-leer.de • Understanding of advanced fluid dynamics including three-dimensional, transient and compressible processes • Identifying the significant physical processes, defining the dimensionality and relevant scales in time and space • Theory of similarity, range of dimensionless numbers • Potential Theory • Numerical Algorithms and possibilities of independent coding of simplest mathematical models • Limitations of numerical models, risk of empirical approaches included in numerical models • Introduction of a complete chain of Open-Source-CFD-Tools, considering preprocessing, processing and postprocessing tools • Need and availability of appropriate measurement techniques for the steering, calibration and verification of models • Contactless high-resolving measuring techniques in the fluid dynamics • Limits of accuracy of different modelling and simulation concepts
  • No access 5.04.4675 - Optical Simulation and Modelling (Zemax) Show lecturers
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    Wednesday: 12:00 - 14:00, weekly (from 19/10/22)
    lecture and project
Exercises
  • No access 5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Till Kahlke

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)
  • No access 5.04.4213 Ü2 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Filippos Panagiotou

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)
  • No access 5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke
    • Dmytro Velychko

    Tuesday: 16:15 - 17:45, weekly (from 25/10/22)
  • No access 5.04.4571 Ü1 - Density-functional theory Show lecturers
    • Prof. Dr. Caterina Cocchi
    • Dr. Ana Maria Valencia Garcia

    Thursday: 16:15 - 17:45, weekly (from 20/10/22)
    Dates on Wednesday, 16.11.2022 14:15 - 15:45
  • No access 5.04.4665 - Modelling and Simulation Show lecturers
    • Jann Strybny
    • Arne Daniel

    Monday: 08:45 - 12:00, weekly (from 17/10/22)
    Contact: jann.strybny@hs-emden-leer.de arne.daniel@hs-emden-leer.de • Understanding of advanced fluid dynamics including three-dimensional, transient and compressible processes • Identifying the significant physical processes, defining the dimensionality and relevant scales in time and space • Theory of similarity, range of dimensionless numbers • Potential Theory • Numerical Algorithms and possibilities of independent coding of simplest mathematical models • Limitations of numerical models, risk of empirical approaches included in numerical models • Introduction of a complete chain of Open-Source-CFD-Tools, considering preprocessing, processing and postprocessing tools • Need and availability of appropriate measurement techniques for the steering, calibration and verification of models • Contactless high-resolving measuring techniques in the fluid dynamics • Limits of accuracy of different modelling and simulation concepts
  • No access 5.04.4675 - Optical Simulation and Modelling (Zemax) Show lecturers
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    Wednesday: 12:00 - 14:00, weekly (from 19/10/22)
    lecture and project
Hinweise zum Modul
Prerequisites
basic programming skills (matlab, python, C/C++)
Module examination
According selected course
Skills to be acquired in this module
Computational Fluid Dynamics (CFD I & II)
  • Deeper understanding of the fundamental equations of fluid dynamics.
  • Overview of numerical methods for the solution of the fundamental equations of fluid dynamics.
  • Confrontation with complex problems in fluiddynamics.
  • To become acquainted with different, widely used CFD models that are used to study complex problems in fluid dynamics.
  • Ability to apply these CFD models to certain defined problems and to critically evaluate the results of numerical models.


Computerorientierte Physik
Extension and complement of qualification in theoretical physics through the acquisition of solid and deep knowledge of advanced concepts and methods in theoretical physics. Depending on the selected course the students acquire knowledge in the fields of basis numerical methods of theoretical physics, algorithms and data structures in scientific computing, code debugging. They obtain skills for a confident application of modern methods of theoretical physics such as diagram generation, Molecular Dynamics and Monte Carlo simulations and quantitative analysis of advanced problems of theoretical physics and in further development of the physical intuition. They enhance their competences to effectively deal with sophisticated problems of theoretical physics, to independently develop approaches to current issues of theoretical physics, and to comprehend common concepts and methods of theoretical physics and the natural sciences, in general.

Modelling and Simulation
The students attending successful the course acquire an advanced understanding of the conceptual design of models in the field of engineering sciences. Special emphasis is on identifying the significant physical processes and the choice of the most efficient modelling type. The interaction of numerical simulations with field measurements and laboratory measurements including the theory of similarity will be discussed. To meet the needs of renewable energy, laser technology, environmental sciences and marine sciences the practical focus is on the modelling and simulation of fluid dynamics in small scales and close to structures.

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