phy611 - Theoretical Methods

phy611 - Theoretical Methods

Institute of Physics 6 KP
Module components Semester courses Summer semester 2024 Examination
Lecture
  • Unlimited access 5.04.4012 - Informationsverarbeitung und Kommunikation / Information Processing and Communication Show lecturers
    • Priv.-Doz. Dr. Jörn Anemüller

    Thursday: 10:00 - 12:00, weekly (from 04/04/24)
    Course topics: - Information processing in the brain, neurons, receptive fields - Simple classification models, the perceptron, linear discriminant analysis, regression approach to classification - Generative approaches, k-nearest neighbour classification, Bayes equation - Model selection and cross-validation - Logistic regression, binary cross-entropy loss function, gradient descent - Gradient descent optimization and regularization, multi-layer perceptron and error backpropagation - Convolutional networks, deep neural networks, receptive fields in deep netoworks - Reinforcement learning - Sequence modeling, speech recognition, markov chains, hidden markov model (HMMs) - Transformer deep networks, large language models (LLMs), from HMMs to LLMs - Information theory, measuring information, entropy - Information theory continued, entropy bound for coding, compression The course language is English or German, with English used by default and German used in case of only German native language speakers taking the course.
  • Unlimited access 5.04.4586 - Digital Signal Processing Show lecturers
    • Prof. Dr. Simon Doclo

    Monday: 16:00 - 18:00, weekly (from 08/04/24)
    Engineering Physics: Alternative für Signal- und Systemtheorie
Exercises
  • Unlimited access 5.04.4012 Ü1 - Übung zu Informationsverarbeitung und Kommunikation / Information Processing and Communication Show lecturers
    • Priv.-Doz. Dr. Jörn Anemüller
    • Eike Jannik Nustede, M. Sc.

    Tuesday: 16:00 - 18:00, weekly (from 09/04/24)
    Die Studierenden erlernen, wie statistische Eigenschaften von Signalen zur Lösung von Problemen der Angewandten Physik, insbesondere der Klassifikation, parametrischen Modellierung und Übertragung von Signalen genutzt werden können. Theoretische Lernziele beinhalten damit eine Wiederholung und Festigung statistischer Grundlagen und eine Verständnis von deren Nutzung für Algorithmen unterschiedlicher Zielsetzung und Komplexität. Im praktischen Teil werden Eigenschaften der behandelten Methoden selbständig erarbeitet sowie Algorithmen auf dem Rechner implementiert und auf reale Daten angewendet, so daß der Umgang mit theoretischen Konzepten und ihre praktische Umsetzung erlernt werden. Inhalte: Grundfragen der Informationsverarbeitung (Klassifikation, Regression, Clustering), Lösungsmethoden basierend auf Dichteschätzung und diskriminativen Ansätzen (z.B. Bayes Schätzung, k-nearest neighbour, Hauptkomponentenanalyse, support-vector-machines, Hidden-Markov- Modelle), Grundlagen der Informationstheorie, Methoden der analogen und digitalen Nachrichtenübertragung, Prinzipien der Kanalcodierung und Kompression
  • Unlimited access 5.04.4586 Ü1 - Exercises to Digital Signal Processing Show lecturers
    • Wiebke Middelberg, M. Sc.
    • Klaus Brümann

    Wednesday: 12:00 - 14:00, weekly (from 03/04/24)
    Engineering Physics: Alternative für Signal- und Systemtheorie
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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