phy611 - Theoretical Methods (Veranstaltungsübersicht)

phy611 - Theoretical Methods (Veranstaltungsübersicht)

Institut für Physik 6 KP
Modulteile Semesterveranstaltungen Wintersemester 2021/2022 Prüfungsleistung
Vorlesung
  • Kein 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), Ort: W32 0-005
    Termine am Dienstag, 08.03.2022 - Freitag, 11.03.2022 08:30 - 18:30, Montag, 14.03.2022 08:30 - 16:00, Dienstag, 15.03.2022 08:30 - 13:00, Montag, 30.05.2022 11:00 - 18:00, Dienstag, 31.05.2022 08:00 - 18:00, Ort: W01 0-012, W04 1-171

    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

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

    Termine am Dienstag, 19.10.2021, Dienstag, 02.11.2021 18:15 - 19:45, Donnerstag, 11.11.2021 16:15 - 19:45, Dienstag, 16.11.2021 18:15 - 19:45, Donnerstag, 25.11.2021 16:15 - 19:45, Dienstag, 11.01.2022 18:15 - 19:45, Montag, 21.02.2022 13:15 - 19:00, Montag, 21.02.2022 14:15 - 19:00, Dienstag, 22.02.2022 13:15 - 19:00, Dienstag, 22.02.2022 14:15 - 19:00, Mittwoch, 23.02.2022 13:15 - 19:00 ...(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

  • Kein 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)

  • Kein 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

  • Kein 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
  • Kein 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), Ort: W32 0-005
    Termine am Dienstag, 08.03.2022 - Freitag, 11.03.2022 08:30 - 18:30, Montag, 14.03.2022 08:30 - 16:00, Dienstag, 15.03.2022 08:30 - 13:00, Montag, 30.05.2022 11:00 - 18:00, Dienstag, 31.05.2022 08:00 - 18:00, Ort: W01 0-012, W04 1-171

    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

  • Kein 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

  • Kein 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)

  • Kein Zugang 5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning Lehrende anzeigen
    • Prof. Dr. Jörg Lücke
    • Dmytro Velychko

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

  • Kein 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

  • Kein 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
Teilnahmevoraussetzungen
basic programming skills (matlab, python, C/C++)
Prüfungsleistung Modul
Lectures (2 or 4 hours per week) / Exercises (1 or 2 hours per week)
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.