Heidelberg Graduate School HGS MathComp

HGS MathComp Curriculum

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Core Courses
Compact Courses
info  Spatial and Temporal Analyses of Geographic Phenomena (STAP19)Various Speakers April 1-4, 2019 ECTS-Points: 2
Abstract, registration & information:
Organizers:
Katharina Anders, Bernhard Höfle, Hubert Mara

Compact Course & Workshop:
- Automatic methods for 3D geospatial data processing
- Geographic applications of 3D data analysis
- Hands-on: 3D point cloud and mesh analysis
- Programming and research challenge: Development of computational methods for 3D information extraction

Invited speakers:
- Prof. Dr. Andreas Nüchter, University of Würzburg
- Jorge Martínez Sánchez, University of Santiago de Compostela

Registration:
Please register on the website of the Compact Course until February 15, 2019
www.uni-heidelberg.de/stap19

Project Auto3Dscapes:
www.uni-heidelberg.de/auto3Dscapes

Contact: Katharina Anders
katharina.anders@uni-heidelberg.de

Twitter:
#STAP19
Link for more information
Location:
Mathematikon, Conference Room, 5th Floor, Room 5/104, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
2
info  Stochastic modeling - Methods, effects and calibration with applications to epidemics and systems biologyDr. Christoph Zimmer, Bosch Center for Artificial Intelligence July 2-3, 2019 ECTS-Points: 2
Abstract, registration & information:
Computational modeling has become more and more important in the life sciences. One important class of models are compartment models and ordinary differential equations are widely used to describe the time evolution of the model components in a deterministic way. This compact course will introduce stochastic compartmental modeling. The objectives of the course are to learn a) methods that allow to simulate stochastic models and b) which effects so called intrinsic stochasticity can have on systems dynamics. These effects will make it evident that specific calibration techniques are needed in order to be able to cope with stochastic effects and exploit their information. The course will c) give a flavor of how calibration can be performed. Time will also be devoted to let the participants learn d) when stochastic modeling is necessary and beneficial.
This course will consist of lectures as well as practical exercises. Therefore, participants are encouraged to bring laptops (please contact me in case laptop sharing is desired). There is no prior software or programming experience necessary.

Please register here
Location:
Mathematikon, SR12 (5th floor), Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
2

Workshops & Schools
Workshops
info  PIMS-Germany Workshop on Discretization of Variational Eigenvalue ProblemsVarious Speakers June 24-26, 2019 ECTS-Points: not yet determined
Abstract, registration & information:
Recent years have seen intense development of discretization schemes for incompressible flow problems in two directions. On the one hand, pairs of discrete spaces with a commuting diagram property for the divergence operator have been developed, with the result that it is now possible to compute actually divergence free solutions with reasonable effort. In particular for high Reynolds number flow, this property is important, since the control of the divergence by the gradient is too weak. Alternatively, penalization of the divergence has been investigated thoroughly with the same goal of achieving better solutions for high Reynolds numbers. On the other hand, replacements for the pressure Poisson problem have been developed, allowing for much faster projection of approximate solutions into the divergence free subspace, in particular with high performance computing in mind. The study of stability of such flows and of critical modes requires the solution of nonsymmetric variational eigenvalue problems. Critical modes are characterized by eigenvalues with small real part, which again may suffer from spurious divergence. In order to approximate such eigenvalues, many flow problems must be solved iteratively, which brings fast solvers back into the game.

The goal of this workshop is convening top researchers in the fields of flow and eigenvalue problems in order to understand the interplay of the interacting components better and to profit from recent research of groups with different focus. Furthermore, its aim is intensifying cooperation between the members of PIMS and universities in Germany with a clear focus on common interest.
Link for more information
Location:
Mathematikon, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
not yet determined
info  PIMS - Germany Workshop on Modeling and Analysis of PDEs for Biological ApplicationsVarious Speakers June 24-26, 2019 ECTS-Points: not yet determined
Abstract, registration & information:
This mini-workshop will bring together experts in modeling and analysis of organizing principles of multiscale biological systems such as cell assemblies, tissues and populations, and collective dynamics of cells. We will focus on questions arising in systems biology and medicine which are related to emergence, function and control of spatial structures, cell-cell interactions, and inter-individual heterogeneity in biological dynamics. Mechanisms of symmetry breaking and establishment of spatial patterns in gene expression leading to different differentiation programmes are central issues of developmental biology, while the understanding of their perturbation and deregulation leading to abnormal development is important in cancer research. Evolution of large scale spatial patches such as, for example, systems of vegetation patterns observed in drylands, is essential for ecosystems. Dynamics of appearance and disappearance of such patterns have a direct economic impact. Spatio-temporal dynamics arising through a diffusion field is also a central theme in characterizing the collective response of microbial particles.

Pattern formation is also an important topic in materials science. For example, the nature of formation and evolution of nano-scale structures in energy conversion devices such as fuel cells and solar cells is decisive for the quality of the performance of these devices. Though some of these patterns are well characterized, there are other that we are only beginning to understand. Mathematical modeling is a powerful technique to address key questions and paradigms in diverse model systems and to provide quantitative insights into the role of the nonlinear and nonlocal interactions within the systems and with the external fields as well as of the growth and transport processes and their impact on the observed patterns.
Although applied to specific biological, ecological, chemical, medical or physical systems, mathematical models allow for a comparative analysis of design principles in diverse systems. The focus of this proposed conference is to present and analyze models of partial and integro-differential equations applied to problems of spatio-temporal patterning. The goal of the meeting is to bring together specialists in Germany and from PIMS universities working on different aspects of the field, including mathematical modeling and applications, analysis of the underlying equations as well as numerical simulation, in order to exchange ideas, present new techniques, and identify challenging new research directions of common interest. The focus will be in identifying and understanding of mechanisms of pattern formation including formation of travelling waves, stationary and dynamical patterns, the effect of mechanical-chemical forces on patterns, stability and bifurcation theory, mechanisms underlying collective dynamics in cell signalling, and the emergence of singularities. Applications to developmental biology, ecology, cell-signalling, and materials science will be presented and discussed.

The outcome of the workshop will be two-fold. Firstly, various mathematical methods and techniques presented for diverse types of model PDE sytems in biology, will lead to cross-fertilization and will help solving in tackling problems related to different applications. Secondly, this workshop will identify common research interests and establish new research collaborations on specific projects among researchers at PIMS and at Universities in Heidelberg, Munster and Berlin.
Link for more information
Location:
Mathematikon, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
not yet determined
info  Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific ComputingVarious Speakers July 1-5, 2019 ECTS-Points: not yet determined
Abstract, registration & information:
Scientific computing concerns the development of mathematical models and high-performance software able to describe, simulate and learn the behaviour of complex phenomena. Applications can arise from any area of applied sciences (e.g. engineering, physics, biology, chemistry) and typically retain the challenging task of quantifying high-dimensional uncertainty due to known unknowns and unknown unknowns present in the natural system. When this is added to the computational burden of approximating and solving complex mathematical models, the application of standard inference algorithms, e.g. for parameter estimation, prediction or optimization, becomes quickly unfeasible within a reasonable computational budget.

The aim of the workshop is to bring together researchers working in Uncertainty Quantification, Machine Learning and Bayesian Statistics with a particular focus on high- and infinite-dimensional problems from scientific computing, where the sparsity or uncertainty of data requires an integration of inference and learning algorithms with established physical models, such as partial differential equations. Advances in this complex field of research require a concerted effort from many disciplines, which we hope to foster at the workshop.

This workshop is part of the Thematic Semester Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific Computing at MAThematics Center Heidelberg (MATCH) in conjunction with the Excellence Cluster STRUCTURES. The financial support from MATCH and from the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) is gratefully acknowledged.

Registration required!
Link for more information
Location:
Mathematikon, Conference Room, 5th Floor, Room 5/104, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
not yet determined
Schools
info  IWR School "A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (ML4Nature)"Various Speakers September 23-27, 2019 ECTS-Points: 3
Abstract, registration & information:
The IWR School 2019 gives a crash course in machine learning with applications from Natural Sciences and Life Sciences. We target young researchers from Natural Sciences and Life Sciences who want to learn more about machine learning. A background in machine learning is not required. Besides introducing the basic concepts of machine learning, we teach selected topics in more depth, such as deep learning, metric learning, transfer learning, Bayesian inverse problems, and causality. Experts from machine learning, Natural Science and Life Science explain how these machine learning approaches are utilized to solve problems in their respective fields of research.

Target Audience:

Postgraduate students, PhD candidates, postdocs and young researchers:

- from Natural and Life Sciences: Microscopy, Biology, Medical, Physics,…
- with interest in Machine Learning
- Master students from Heidelberg University (core course listed in LSF)

Speakers:

The IWR School 2019 is taught in a series of courses and single lectures by:

- Christoph Lampert, Institute of Science and Technology Austria
- Oliver Stegle, European Bioinformatics Institute
- Robert Scheichl, Heidelberg University
- Dominik Janzing, Max Planck Institute for Intelligent Systems
- Klaus Maier Hein, German Cancer Research Center
- Bjoern Ommer, Heidelberg University
- Ullrich Köthe, Heidelberg University
- Anna Kreshuk, European Molecular Biology Laboratory

For more information please visit the website of the IWR School 2019.
Link for more information
Location:
Mathematikon, Conference Room, 5th Floor, Room 5/104, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
8:00
ECTS-Points:
3

Further Studies
Seminars
info  Tools SeminarVarious Speakers September 30 - October 2, 2019 ECTS-Points: 0
Abstract, registration & information:
The Tools Seminar provides an opportunity for scientists and students to get proficient information about certain tools useful for their study or research and to exchange their experience and knowledge about those tools with colleagues and fellow students. The term "tool" is understood in a broad sense ranging from tools useful when developing software to more general issues like how to give a good presentation. Particularly, the aim of the Tools Seminar is to provide profound information which go beyond the basic concepts that many people might already be familiar with. But each talk will also include at least a short introduction to allow the participants to learn about tools they might not have used before. So, no matter which level of experience you have with the tools presented, you should be able to learn something new in this seminar!


All talks will be in English!


The preliminary schedule and the registration form can be found here. The participation in the seminar is free of charge, but please register using the registration form for organizational reasons if you plan to participate!

Registration form
Link for more information
Location:
Mathematikon, 5th Floor, Conference Room (Room 5/104), Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
0
Block Lectures
info  Markov Chain Monte Carlo for Inverse Problems in PDEsProf. Colin Fox, Romberg Visiting Scholar July 22 & July 24-26, 2019, 9:00-12:00 & 14:00-15:00 ECTS-Points: 3
Abstract, registration & information:
His short course will cover some first steps in performing computational sample-based inference in inverse problems where the forward map requires solving a PDE (partial differential equation). We will look at some MCMC (Markov chain Monte Carlo) algorithms for drawing samples that are distributed according to the resulting posterior distribution, in few and many dimensions, from simple to state of the art. We will also introduce some basic PDE solvers, and discuss the important finite-rank property of the associated forward map. In the last lecture we will discuss mid-level and high-level models, that are the future of this field. The accompanying practical computer sessions allows participants to get hands-on experience with all these topics.

Please register here
Location:
Mathematikon, Conference Room, 5th Floor, Room 5/104 & SR12, Im Neuenheimer Feld 205, 69120 Heidelberg
Time:
9:00
ECTS-Points:
3