Helwig Hauser

Rainer Splechtna, Majid Behravan, Mario Jelovic et al.

The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space. The extent of the parameter space, the complexity of the non-linear model of the system, as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other. The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation. When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.

Katarína Furmanová, Adam Jurčík, Barbora Kozlíková et al.

When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data -- from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels and at each level, we offer a set of selection and filtering operations enabling the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.

Bernhard Preim, Paul Klemm, Helwig Hauser et al.

Epidemiology characterizes the influence of causes to disease and health conditions of defined populations. Cohort studies are population-based studies involving usually large numbers of randomly selected individuals and comprising numerous attributes, ranging from self-reported interview data to results from various medical examinations, e.g., blood and urine samples. Since recently, medical imaging has been used as an additional instrument to assess risk factors and potential prognostic information. In this chapter, we discuss such studies and how the evaluation may benefit from visual analytics. Cluster analysis to define groups, reliable image analysis of organs in medical imaging data and shape space exploration to characterize anatomical shapes are among the visual analytics tools that may enable epidemiologists to fully exploit the potential of their huge and complex data. To gain acceptance, visual analytics tools need to complement more classical epidemiologic tools, primarily hypothesis-driven statistical analysis.

Ivan Kolesar, Julius Parulek, Ivan Viola et al.

Research in cell biology is steadily contributing new knowledge about many different aspects of physiological processes like polymerization, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments. In this paper, we contribute a new, three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. We integrate physical and empirical modeling, according to which approach suits the different involved levels of detail best, and we additionally enable a simple form of interactive steering while the process is illustrated. We demonstrate the suitability of our approach in the context of several polymerization processes and report from a first evaluation with domain experts.