Stud.IP Uni Oldenburg
Universität Oldenburg
08.12.2021 08:40:59
phy611 - Theoretical Methods (Veranstaltungsübersicht)
Institut für Physik 6 KP
Modulteile Semesterveranstaltungen Wintersemester 2021/2022 Prüfungsleistung
Vorlesung
  • Uneingeschränkter Zugang 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke

    Mittwoch: 10:15 - 11:45, wöchentlich (ab 20.10.2021)

    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

  • Uneingeschränkter Zugang 5.04.4521 - Computerorientierte Physik Lehrende anzeigen
    • Prof. Dr. Alexander Hartmann

    Termine am Dienstag. 19.10.21, Dienstag. 02.11.21 18:15 - 19:45, Donnerstag. 11.11.21 16:15 - 19:45, Dienstag. 16.11.21 18:15 - 19:45, Donnerstag. 25.11.21 16:15 - 19:45, Dienstag. 11.01.22 18:15 - 19:45, Montag. 21.02.22 10:15 - 11:45, Montag. 21.02.22 13:15 - 14:00, Montag. 21.02.22 14:15 - 19:00, Dienstag. 22.02.22 10:15 - 11:45, Dienstag. 22.02.22 13:15 - 14:00 ...(mehr)
    Ort: W02 1-148, 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

  • Uneingeschränkter Zugang 5.04.4571 - Density-functional theory Lehrende anzeigen
    • Prof. Dr. Caterina Cocchi

    Dienstag: 14:15 - 15:45, wöchentlich (ab 19.10.2021)
    Donnerstag: 10:15 - 11:45, wöchentlich (ab 21.10.2021)

  • Eingeschränkter Zugang 5.04.4665 - Modelling and Simulation Lehrende anzeigen
    • Jann Strybny
    • Arne Daniel

    Montag: 09:45 - 13:00, wöchentlich (ab 25.10.2021)

    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

  • Uneingeschränkter Zugang 5.04.4675 - Optical Simulation and Modelling (Zemax) Lehrende anzeigen
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    Montag: 17:00 - 19:00, wöchentlich (ab 18.10.2021)

    lecture and project

Übung
  • Uneingeschränkter Zugang 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke

    Mittwoch: 10:15 - 11:45, wöchentlich (ab 20.10.2021)

    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

  • Uneingeschränkter Zugang 5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Florian Hirschberger
    • Filippos Panagiotou

    Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021), Ort: W32 0-005, W32 1-112

  • Uneingeschränkter Zugang 5.04.4213 Ü2 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Filippos Panagiotou
    • Florian Hirschberger

    Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021)

  • Uneingeschränkter Zugang 5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Florian Hirschberger
    • Filippos Panagiotou

    Dienstag: 10:15 - 11:45, wöchentlich (ab 26.10.2021), Ort: W04 1-172, W01 0-012

  • Eingeschränkter Zugang 5.04.4665 - Modelling and Simulation Lehrende anzeigen
    • Jann Strybny
    • Arne Daniel

    Montag: 09:45 - 13:00, wöchentlich (ab 25.10.2021)

    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

  • Uneingeschränkter Zugang 5.04.4675 - Optical Simulation and Modelling (Zemax) Lehrende anzeigen
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    Montag: 17:00 - 19:00, wöchentlich (ab 18.10.2021)

    lecture and project

Hinweise zum Modul
Prüfungsleistung Modul
According selected course
Kompetenzziele
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.