Search result: Catalogue data in Spring Semester 2021

Computational Science and Engineering Master Information
Fields of Specialization
Electromagnetics
recognition of 227-0662-00L and 227-0662-10L requires the successful completion of both course units
NumberTitleTypeECTSHoursLecturers
227-0622-00LThermal Modeling: From Semiconductor to Medical Devices and Personalized Therapy PlanningW4 credits2V + 1UE. Neufeld, M. Luisier
AbstractThe course introduces computational techniques to model electromagnetic heating across many orders of magnitudes, from the atomic to the macroscopic scale. Both desired and undesired thermal effects will be covered, e.g. thermal cancer therapies based on tissue heating or Joule heating in semiconductor devices. A wide range of simulation approaches and numerical methods will be introduced.
ObjectiveDuring this course the students will:

- learn the physics governing and computational models describing electromagnetic-induced heating;

- get familiar with computational simulation techniques across a wide range of spatial scales, incl. methods to simulate in vivo heating, considering thermoregulation and perfusion, or quantum mechanical approaches considering heat at the level of atomic vibrations;

- implement and apply simulation techniques within a state-of-the-art open-source simulation platform for computational life sciences, as well as a framework for computer-aided design of semiconductor devices;

- learn about remaining challenges in this field
ContentThe following topics will be discussed during the semester:

- Introduction about electromagnetic heating (from its historical perspective to its application in biology);

- Microscopic/Macroscopic thermal transport (governing equations, numerical methods, examples);

- Numerical algorithms and their implementation in python and/or C++, parallelisation approaches, and high performance computing solutions;

- Practical examples: thermal therapy planning with Sim4Life and technology computer aided design with OMEN;

- Model verification and validation.
Lecture notesLecture slides are distributed every week and can be found at
Link
Prerequisites / NoticeThe course requires an open attitude towards interdisciplinarity, basic python scripting and C++ coding skills, undergraduate entry-level familiarity with electric & magnetic fields/forces, differential equations, calculus, and basic knowledge of biology and quantum mechanics.
227-0707-00LOptimization Methods for EngineersW3 credits2GJ. Smajic
AbstractFirst half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc.
Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest.
ObjectiveNumerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems.
ContentTypical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes.
Lecture notesPDF of a short skript (39 pages) plus the view graphs are provided
Prerequisites / NoticeLecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester.
401-5870-00LSeminar in Electromagnetics for CSEW4 credits2SJ. Smajic, J. Leuthold
AbstractDiscussion of fundamentals of electromagnetics and various applications (wave propagation, scattering, antennas, waveguides, bandgap materials, etc.). Numerical methods suited for the analysis of electromagnetic fields and for the optimal design of electromagnetic structures.
ObjectiveKnowledge about classical electromagnetics, main applications, and appropriate numerical methods.
Prerequisites / NoticeStudents study a selected topic and give a 15-30 minutes presentation towards the end of the semester. The topic and the supervisor is defined in a discussion with J. Smajic or J. Leuthold.
Geophysics
Recommended combinations:
Subject 2 + Subject 5 + Subject 6 + Subject 7
Subject 2 + Subject 4 + Subject 5 + Subject 6 + Subject 8
Subject 2 + Subject 5 + Subject 6 + (Subject 1 or Subject 3)
Geophysics: Subject 1
offered in the autumn semester
Geophysics: Subject 2
offered in the autumn semester
Geophysics: Subject 3
NumberTitleTypeECTSHoursLecturers
651-4008-00LDynamics of the Mantle and LithosphereW3 credits2GA. Rozel
AbstractThe goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets.
ObjectiveThe goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets.
Geophysics: Subject 4
NumberTitleTypeECTSHoursLecturers
651-4094-00LNumerical Modelling for Applied GeophysicsW5 credits2GJ. Robertsson, H. Maurer
AbstractNumerical modelling in environmental and exploration geophysics. The course covers different numerical methods such as finite difference and finite element methods applied to solve PDE’s for instance governing seismic wave propagation and geoelectric problems.

Prerequisites include basic knowledge of (i) signal processing and applied mathematics such as Fourier analysis and (ii) Matlab.
ObjectiveAfter this course students should have a good overview of numerical modelling techniques commonly used in environmental and exploration geophysics. Students should be familiar with the basic principles of the methods and how they are used to solve real problems. They should know advantages and disadvantages as well as the limitations of the individual approaches.

The course includes exercises in Matlab where the stduents both should lear, understand and use existing scripts as well as carrying out some coding in Matlab themselves.
ContentDuring the first part of the course, the following topics are covered:
- Applications of modelling
- Physics of acoustic, elastic, viscoelastic wave equations as well as Maxwell's equations for electromagnetic wave propagation and diffusive problems
- Recap of basic techniques in signal processing and applied mathematics
- Potential field modelling
- Solving PDE's, boundary conditions and initial conditions
- Acoustic/elastic wave propagation I, explicit time-domain finite-difference methods
- Acoustic/elastic wave propagation II, Viscoelastic, pseudospectral
- Acoustic/elastic wave propagation III, spectral accuracy in time, frequency domain FD, Eikonal
- Implicit finite-difference methods (geoelectric)
- Finite element methods, 1D/2D (heat equation)
- Finite element methods, 3D (geoelectric)
- Acoustic/elastic wave propagation IV, Finite element and spectral element methods
- HPC and current challenges in computational seismology
- Seismic data imaging project

Most of the lecture modules are accompanied by exercises Small projects will be assigned to the students. They either include a programming exercise or applications of existing modelling codes.
Lecture notesPresentation slides and some background material will be provided.
LiteratureIgel, H., 2017. Computational seismology: a practical introduction. Oxford University Press.
Prerequisites / NoticeThis course is offered as a full semester course. During the second part of the semester some lecture slots will be dedicated towards working on exercises and course projects.
Geophysics: Subject 6
NumberTitleTypeECTSHoursLecturers
651-4006-00LSeismology of the Spherical EarthW3 credits3GM. van Driel, S. C. Stähler
AbstractBrief review of continuum mechanics and the seismic wave equation; P and S waves; reciprocity and representation theorems; eikonal equation and ray tracing; Huygens and Fresnel; surface-waves; normal-modes; seismic interferometry and noise; numerical solutions.
ObjectiveAfter taking this course, students will have the background knowledge necessary to start an original research project in quantitative seismology.
LiteratureShearer, P., Introduction to Seismology, Cambridge University Press,
1999.

Aki, K. and P. G. Richards, Quantitative Seismology, second edition,
University Science Books, Sausalito, 2002.

Nolet, G., A Breviary of Seismic Tomography, Cambridge University Press, 2008.
Prerequisites / NoticeThis is a quantitative lecture with an emphasis on mathematical description of wave propagation phenomena on the global scale, hence basic knowledge in vector calculus, linear algebra and analysis as well as seismology (e.g. from the 'wave propagation' lecture) are essential to follow this course.
Geophysics: Subject 7
NumberTitleTypeECTSHoursLecturers
651-4096-00LInverse Theory I: BasicsW3 credits2VA. Fichtner
AbstractInverse theory is the art of inferring properties of a physical system from noisy and sparse observations. It is used to transform observations of waves into 3D images of a medium seismic tomography, medical imaging and material science; to constrain density in the Earth from gravity; to obtain probabilities of life on exoplanets ... . Inverse theory is at the heart of many natural sciences.
ObjectiveThe goal of this course is to enable students to develop a mathematical formulation of specific inference (inverse) problems that may arise anywhere in the physical sciences, and to implement suitable solution methods. Furthermore, students should become aware that nearly all relevant inverse problems are ill-posed, and that their meaningful solution requires the addition of prior knowledge in the form of expertise and physical intuition. This is what makes inverse theory an art.
ContentThis first of two courses covers the basics needed to address (and hopefully solve) any kind of inverse problem. Starting from the description of information in terms of probabilities, we will derive Bayes' Theorem, which forms the mathematical foundation of modern scientific inference. This will allow us to formalise the process of gaining information about a physical system using new observations. Following the conceptual part of the course, we will focus on practical solutions of inverse problems, which will lead us to study Monte Carlo methods and the special case of least-squares inversion.

In more detail, we aim to cover the following main topics:

1. The nature of observations and physical model parameters
2. Representing information by probabilities
3. Bayes' theorem and mathematical scientific inference
4. Random walks and Monte Carlo Methods
5. The Metropolis-Hastings algorithm
6. Simulated Annealing
7. Linear inverse problems and the least-squares method
8. Resolution and the nullspace
9. Basic concepts of iterative nonlinear inversion methods

While the concepts introduced in this course are universal, they will be illustrated with numerous simple and intuitive examples. These will be complemented with a collection of computer and programming exercises.

Prerequisites for this course include (i) basic knowledge of analysis and linear algebra, (ii) basic programming skills, for instance in Matlab or Python, and (iii) scientific curiosity.
Lecture notesPresentation slides and detailed lecture notes will be provided.
Prerequisites / NoticeThis course is offered as a half-semester course during the first part of the semester
651-4096-02LInverse Theory II: Applications
Prerequisites: The successful completion of 651-4096-00L Inverse Theory I: Basics is mandatory.
W3 credits2GA. Fichtner, C. Böhm
AbstractThis second part of the course on Inverse Theory provides an introduction to the numerical solution of large-scale inverse problems. Specific examples are drawn from different areas of geophysics and image processing. Students solve various model problems using python and jupyter notebooks, and familiarize themselves with relevant open-source libraries and commercial software.
ObjectiveThis course provides numerical tools and recipes to solve (non)-linear inverse problems arising in nearly all fields of science and engineering. After successful completion of the class, the students will have a thorough understanding of suitable solution algorithms, common challenges and possible mitigations to infer parameters that govern large-scale physical systems from sparse data measurements.

Prerequisites for this course are (i) 651-4096-00L Inverse Theory: Basics, (ii) basic programming skills.
ContentThe class discusses several important concepts to solve (non)-linear inverse problems and demonstrates how to apply them to real-world data applications. All sessions are split into a lecture part in the first half, followed by tutorials using python and jupyter notebooks in the second. The range of covered topics include:

1. Regularization filters and image deblurring
2. Travel-time tomography
3. Line-search methods
4. Time reversal and Born’s approximation
5. Adjoint methods
6. Full-waveform inversion
Lecture notesPresentation slides and some background material will be provided.
Prerequisites / NoticeThis course is offered as a half-semester course during the second part of the semester
Geophysics: Subject 8
offered in the autumn semester
Geophysics: Seminar
NumberTitleTypeECTSHoursLecturers
401-5880-00LSeminar in Geophysics for CSEW4 credits2ST. Gerya, P. Tackley
Abstract
Objective
Biology
NumberTitleTypeECTSHoursLecturers
636-0702-00LStatistical Models in Computational BiologyW6 credits2V + 1U + 2AN. Beerenwinkel
AbstractThe course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
ObjectiveThe goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
ContentGraphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Lecture notesno
Literature- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252
- Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007.
- Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004
701-1708-00LInfectious Disease DynamicsW4 credits2VS. Bonhoeffer, R. D. Kouyos, R. R. Regös, T. Stadler
AbstractThis course introduces into current research on the population biology of infectious diseases. The course discusses the most important mathematical tools and their application to relevant diseases of human, natural or managed populations.
ObjectiveAttendees will learn about:
* the impact of important infectious pathogens and their evolution on human, natural and managed populations
* the population biological impact of interventions such as treatment or vaccination
* the impact of population structure on disease transmission

Attendees will learn how:
* the emergence spread of infectious diseases is described mathematically
* the impact of interventions can be predicted and optimized with mathematical models
* population biological models are parameterized from empirical data
* genetic information can be used to infer the population biology of the infectious disease

The course will focus on how the formal methods ("how") can be used to derive biological insights about the host-pathogen system ("about").
ContentAfter an introduction into the history of infectious diseases and epidemiology the course will discuss basic epidemiological models and the mathematical methods of their analysis. We will then discuss the population dynamical effects of intervention strategies such as vaccination and treatment. In the second part of the course we will introduce into more advanced topics such as the effect of spatial population structure, explicit contact structure, host heterogeneity, and stochasticity. In the final part of the course we will introduce basic concepts of phylogenetic analysis in the context of infectious diseases.
Lecture notesSlides and script of the lecture will be available online.
LiteratureThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Keeling & Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton Univ Press 2008
* Anderson & May, Infectious Diseases in Humans, Oxford Univ Press 1990
* Murray, Mathematical Biology, Springer 2002/3
* Nowak & May, Virus Dynamics, Oxford Univ Press 2000
* Holmes, The Evolution and Emergence of RNA Viruses, Oxford Univ Press 2009
Prerequisites / NoticeBasic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.
262-0200-00LBayesian Phylodynamics – Taming the BEASTW4 credits2G + 2AT. Stadler, T. Vaughan
AbstractHow fast is COVID-19 spreading at the moment? How fast was Ebola spreading in West Africa? Where and when did these epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions.
ObjectiveAttendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are:
* Bayesian statistics
* Phylogenetic and phylodynamic models
* Markov Chain Monte Carlo methods
Attendees will apply these concepts to a number of applications yielding biological insight into:
* Epidemiology
* Pathogen evolution
* Macroevolution of species
ContentDuring the first part of the block course, the theoretical concepts of Bayesian phylodynamics will be presented by us as well as leading international researchers in that area. The presentations will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We will use previously published datasets on e.g. COVID-19, Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on Link.
In the second part of the block course, students choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. A final written report on the research project has to be submitted after the block course for grading.
Lecture notesAll material will be available on Link.
LiteratureThe following books provide excellent background material:
• Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
• Yang, Z. 2014. Molecular Evolution: A Statistical Approach.
• Felsenstein, J. 2003. Inferring Phylogenies.
More detailed information is available on Link.
Prerequisites / NoticeThis class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.
227-0973-00LTranslational NeuromodelingW8 credits3V + 2U + 1AK. Stephan
AbstractThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.
ObjectiveTo obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data.
ContentThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models.

Lecture topics include:
1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics
2. Psychiatric nosology
3. Pathophysiology of psychiatric disease mechanisms
4. Principles of Bayesian inference and generative modeling
5. Variational Bayes (VB)
6. Bayesian model selection
7. Markov Chain Monte Carlo techniques (MCMC)
8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases
9. Generative models of fMRI data
10. Generative models of electrophysiological data
11. Generative models of behavioural data
12. Computational concepts of schizophrenia, depression and autism
13. Model-based predictions about individual patients

Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required.
LiteratureSee TNU website:
Link
Prerequisites / NoticeGood knowledge of principles of statistics, good programming skills (MATLAB, Julia, or Python)
701-1418-00LModelling Course in Population and Evolutionary Biology Information Restricted registration - show details
Number of participants limited to 20.

Priority is given to MSc Biology and Environmental Sciences students.
W4 credits6PS. Bonhoeffer, V. Müller
AbstractThis course provides a "hands-on" introduction into mathematical/computational modelling of biological processes with particular emphasis on evolutionary and population-biological questions. The models are developed using the Open Source software R.
ObjectiveThe aim of this course is to provide a practical introduction into the modelling of fundamental biological questions. The participants will receive guidance to develop mathematical/computational models in small teams. The participants chose two modules with different levels of difficulty from a list of projects.

The participant shall get a sense of the utility of modelling as a tool to investigate biological problems. The simpler modules are based mostly on examples from the earlier lecture "Ecology and evolution: populations" (script accessible at the course webpage). The advanced modules address topical research questions. Although being based on evolutionary and population biological methods and concepts, these modules also address topics from other areas of biology.
Contentsee Link
Lecture notesDetailed handouts describing both the modelling and the biological background are available to each module at the course website. In addition, the script of the earlier lecture "Ecology and evolution: populations" can also be downloaded, and contains further background information.
Prerequisites / NoticeThe course is based on the open source software R. Experience with R is useful but not required for the course. Similarly, the course 701-1708-00L Infectious Disease Dynamics is useful but not required.
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