Torsten Ullrich studied mathematics at the University Karlsruhe (TH) and received his Ph.D. on "Reconstructive Geometry" in computer science from Graz University of Technology, Austria in 2011. His main research areas are computer graphics in combination with numerical, statistical, and geometric optimization. He has been the project coordinator for various research projects. Currently, he is the Deputy Head of the business area Visual Computing of Fraunhofer Austria Research GmbH. He is responsible for scientific research coordination.
For further details see my Curriculum Vitae.
The objective of global optimization is to find the globally best solution of a model. Nonlinear models are ubiquitous in many applications and their solution often requires a global search approach. Depending on the field of application, the question whether a found solution is not only a local minimum but a global one is very important.
This software implements a probabilistic approach to determine the probability of a solution being a global minimum. The approach is independent of the used global search method and only requires a limited, convex parameter domain as well as a Lipschitz continuous minimization / error function whose Lipschitz constant is not needed to be known.
The software is a Java executable archive. The developed techniques are described in
Eva Eggeling, Dieter W. Fellner, and Torsten Ullrich (2013), Probability of Globality, Proceedings of the International Conference on Computer and Applied Mathematics (ICCAM 2013), 34:144-148.
Torsten Ullrich and Dieter W. Fellner (2014), Statistical Analysis on Global Optimization, Proceeding of the International Conference on Mathematics and Computers in Sciences and Industry, 978-1-4799-4744-7:99-106.
The need to analyze and visualize differences of very similar objects arises in many research areas: mesh compression, scan alignment, nominal/actual value comparison, quality management, and surface reconstruction to name a few. Although the problem to visualize some distances may sound simple, the creation of a good scene setup including the geometry, materials, colors, and the representation of distances is challenging.
Our contribution to this problem is an application which optimizes the work-flow to visualize distances.
The software is a Java executable archive. The implemented techniques are described in
Torsten Ullrich, Volker Settgast, and Dieter W. Fellner (2008), Abstand: Distance Visualization for Geometric Analysis, Project Paper Proceedings of the Conference on Virtual Systems and MultiMedia Dedicated to Digital Heritage (VSMM), 14:334–340.
Generative modeling techniques have rapidly gained attention throughout the past few years. Many researchers enforced the creation of generative models due to its many advantages. All objects with well-organized structures and repetitive forms can be described procedurally. In these cases, generative modeling is superior to conventional approaches.
Its strength lies in the compact description compared to conventional approaches, which does not depend on the counter of primitives but on the model's complexity itself. Particularly large scale models and scenes - such as plants, buildings, cities, and landscapes - can be described efficiently. Therefore generative descriptions make complex models manageable as they allow identifying a shape's high-level parameters.
Euclides differs from other modeling environments in a very important aspect: target independence. In our system, a model's source code is no interpreted but parsed into an intermediate representation. After a validation process it is translated to the target language.