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HGS MathComp - Where Methods Meet Applications

The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) at Heidelberg University is one of the leading graduate schools in Germany focusing on the complex topic of Scientific Computing. Located in a vibrant research environment, the school offers a structured interdisciplinary education for PhD students. The program supports students in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life-sciences, physics, and chemical engineering sciences to cultural heritage. A strong focus is put on the mathematical and computational foundations: the theoretical underpinnings and computational abstraction and conception.

HGS MathComp Principal Investigators are leading experts in their fields, working on projects that combine mathematical and computational methodology with topical research issues. Individual mentoring for PhD candidates and career development programs ensure that graduates are fully equipped to take up top positions in industry and academia.

Upcoming Events [see all...]

20.01.2025
14:00 - 16:20
Theory & Methods
HGS MathComp Membership Colloquium
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Colloquium
Speaker: New HGS MathComp PhD fellows
Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg
Organizer: HGS MathComp
ECTS: 0
Introduction of new HGS MathComp members and their PhD projects.

The BlueSheet Meeting will be held online for all new members of HGS MathComp on January 23, 2025 between 14:00 - 15:00.

14:00 Denis Zavadski (Supervisor C. Rother)
"Controllable Image Synthesis with Text-to-Image Diffusion Models"

14:20 Damjan Kalsan (Supervisor C. Rother)
"Controlled Generation of Labelled Synthetic Data"

14:40 Hannah Rickmann (Supervisor R. Herzog)
"Gaussian Process Regression Models for Predicting Fatigue Strength from Process and Material Data in Machining"

15:00 Short Coffee Break

15:20 Purusharth Saxena (Supervisor B. Velten)
"Optimal Transport and Causal Inference in Gene Regulatory Networks"

15:40 Wangjun Hu (Supervisor B. Velten)
"Generative latent variable models for spatial transcriptomics data"

16:00 Christian Alber (Supervisor R. Scheichl)
"Generalized Finite Elements and Localized Model Order Reduction"
 
29.01.2025
09:00 - 13:00
Theory & Methods
Containers in Science: Using Docker and Singularity
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Compact Courses
Speaker: Dr. Dominic Kempf, Research Software Engineer, Scientific Software Center (SSC)
Location: Mathematikon • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register via this form
Organizer: Scientific Software Center (SSC)
ECTS: 0.5
This is a half day course.

The latest information and a registration link are available on the course website.

This compact course is part of the course program of the Scientific Software Center (SSC) at Heidelberg University.

Prerequisites

Participants should have a basic understanding of the Unix Shell e.g. be able to execute commands and edit files. Participants are required to bring their own laptops to work on during the course. Root privileges and network access (e.g. through Eduroam) on this computer are required. Instructions on how to install Docker on the participants computer will be sent out before the course.

Summary

Container technologies (e.g. Docker containers) have emerged as a fundamental tool of the cloud computing era. In scientific applications, containerization is used to encapsulate the complex execution environment of research software with a number of goals in mind: Setting up user landscapes for Continuous Integration testing, ensuring reproducibility of execution environments and packaging code to run on an HPC system. The workshop involves live coding sessions where participants exercise the learned commands on their own computers.

Learning Objectives

After the course participants will:

- Understand the basic terminology of containerization
- Know where to find and reuse ready-to-use containers
- Know how to create containers for their daily work on their own
- Have built and run a parallel application within a Singularity container
 
29.01.2025
16:15
Theory & Methods
Adjoint-based calibration of nonlinear stochastic differential equations
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IWR Colloquium
Speaker: Prof. Stefan Volkwein • Department of Mathematics and Statistics, University of Konstanz
Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg
Registration: No registration required
Organizer: Interdisciplinary Center for Scientific Computing (IWR)
ECTS: 1 for 5
The IWR Colloquium serves as a platform for the interdisciplinary dialogue which characterizes the field of scientific computing. Every semester, members of the IWR and its affiliated institutions as well as renowned international experts are invited to present their latest scientific results and discuss the upcoming challenges in the field of scientific computing.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom. For more information please visit the website of the colloquium.

To study the nonlinear properties of complex natural phenomena, the evolution of the quantity of interest can be often represented by systems of coupled nonlinear stochastic differential equations (SDEs). These SDEs typically contain several parameters which have to be chosen carefully to match the experimental data and to validate the effectiveness of the model. In the present paper the calibration of these parameters is described by nonlinear SDE-constrained optimization problems. In the optimize-before-discretize setting a rigorous analysis is carried out to ensure the existence of optimal solutions and to derive necessary first-order optimality conditions. For the numerical solution a Monte-Carlo method is applied using parallelization strategies to compensate for the high computational time. In the numerical examples an Ornstein-Uhlenbeck and a stochastic Prandtl-Tomlinson bath model are considered.

This is a joint work with Jan Bartsch and Robert Denk. The associated paper will appear in the Journal Applied Mathematics & Optimization.