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"

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

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.