Welcome

• Anders Kaestner (Paul Scherrer Institut, NIAG)

Visualization and analysis of multidimensional data using morphological techniques (Lectures)

• Jos Roerdink (University of Groningen)

This talk centers around the analysis and visualization of multidimensional scalar and tensor data within the framework of mathematical morphology. Initiated in the 1960s, mathematical morphology was developed to describe image operators for enhancement, segmentation, and extraction of shape information from digital images. In contrast to traditional linear image processing, the morphological image operators focus on the geometrical content of images and are nonlinear. In this talk we first discuss morphological pyramids for multiresolution visualization of volume data. Then we describe recent work on morphological filters for multidimensional tensor-valued data. From the theoretical point of view, an important aspect in the design of morphological operators is their invariance under translation, rotation or scale changes, or, more generally, under an arbitrary group of transformations. A recent approach to group invariance (and particularly rotation invariance) for tensor fields is presented, based on the concept of frames.

Tomviz, ParaView, and VTK: Open, Scalable Visualization and Data Analysis for Tomographic Data

• Marcus Hanwell (Kitware)

Materials tomography involves a number of steps to go from projection images taken on the instrument to an aligned, reconstructed 3D volume. The Tomviz project builds upon a number of open source frameworks to deliver a powerful desktop application for research, leveraging the Python environment along with a number of scientific Python modules to deliver a comprehensive solution for materials tomography at nanoscale to atomic resolution. The development of the application will be discussed, along with the Python-based data processing pipeline, and the XML format used to enable complex, reproducible data processing, segmentation, and visualization pipelines. The application is based on Qt, VTK, ParaView, and ITK with a bundled Python distribution making use of NumPy, SciPy, and Python wrapped ITK/ParaView to offer a powerful visualization and data analysis application. In addition new challenges are emerging as supercomputer architectures become more diverse, and complex. The addition of GPGPU, many-core CPUs, burst buffers and in-situ analysis/visualization lead to the increased need for closer integration of the data analysis and visualization pipeline with simulation codes. Computational power is outstripping I/O bandwidth as we move towards exascale computing, and the importance of in situ processing coupled with strategies for performing processing in burst buffers is more pronounced.

Interactive Visual Analysis in the Material and Computational Sciences (Lectures)

• Eduard Groeller (TU Wien)

Visualization and visual computing use computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity concerning volume, veracity, velocity, and variety has increased considerably. This is due to new data sources as well as the availability of uncertainty, error and tolerance information. Instead of individual objects entire sets, collections, and ensembles are visually investigated. There is a need for visual analyses, comparative visualization, quantitative visualizations, and scalable visualizations. The simultaneous exploration and visualization of spatial and abstract information is an important case in point. Several examples from the material and computational sciences will be discussed in detail. Given the amplified data variability, interactive visual data analyses are likely to gain in importance in the future. Research challenges and directions are sketched at the end of the talk.

Visualization techniques for the analysis of ensemble variability (Lectures)

• Rüdiger Westermann (Technical University Munich)

In many scientific fields, the recognition that predictability is limited has led to a paradigm shift in how predictions of dynamic processes are created. Instead of making a single deterministic computation of the future field state, ensembles of many numerical simulations are computed—based on a set of possible initial states and random variations to account for model uncertainty—and predictions take the form of probabilities of occurrence of specific features derived. In meteorology, ensemble forecasting is used to estimate the uncertainty inherent in the prediction of weather events, by providing a representative sample of the possible states of the atmosphere that could evolve out of perturbed initial conditions and different models. In my talk I will address ensemble visualization techniques, which aim at analysing the variability of an ensemble, so that from the rate of divergence of the individual ensemble members the uncertainty of a single weather forecast can be estimated stochastically. I will shed light on ensemble visualization techniques for specific features in scalar- and vector-valued ensembles, such as iso-contours and particle trajectories, and I will hint on some basic problems we encountered when dealing with ensemble data, such as curve and shape comparison, visual abstractions for ensembles, as well as the visual representation of probabilities.

Tensor Visualization (Lectures)

• Anna Vilanova (TUDelft)

Tensor-field visualization has got special attention of the visualization community in the last decades. An important force on this development is the advance of diffusion weighted magnetic resonance imaging (DW-MRI) acquisition. This MRI modality allows the acquisition of water diffusion information in living tissue. This information measured at the macro level (mm) allows the unprecedented in-vivo visualization of fibrous structures (e.g., white matter fiber bundles) at the microscopic level. The diffusion information can be represented by a second-order positive definite tensor, but also by higher-order descriptors that provide more insight in the complexity of the fibrous structure. In this talk, I will present an overview of the different visualization techniques that have been developed for tensor field visualization focusing on the medical domain, and touch upon uses in material sciences. I will also describe my view on the current main challenges on tensor-field visualization.