https://www.dagstuhl.de/19151

April 7 – 12 , 2019, Dagstuhl Seminar 19151

Visual Computing in Materials Sciences

Organizers

Christoph Heinzl (FH Oberösterreich – Wels, AT)
Robert Michael Kirby (University of Utah – Salt Lake City, US)
Stepan V. Lomov (KU Leuven, BE)
Guillermo Requena (DLR – Köln, DE)
Rüdiger Westermann (TU München, DE)

For support, please contact

Susanne Bach-Bernhard for administrative matters

Shida Kunz for scientific matters

Documents

Dagstuhl Seminar Schedule (Upload here)

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Motivation

Visual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible before. Visual computing techniques generate novel insights to understand, discover, design, and use complex material systems of interest. Its huge potential for retrieving and visualizing (new) information on materials, their characteristics and interrelations as well as on simulating the material's behavior in its target application environment is of core relevance to material scientists. This Dagstuhl seminar on Visual Computing in Materials Sciences thus focuses on the intersection of both domains to guide research endeavors in this field. It targets to provide answers regarding the following four challenges, which are of imminent need:

  • The Integrated Visual Analysis Challenge identifies standard visualization tools as insufficient for exploring materials science data in detail. What is required are integrated visual analysis tools, which are tailored to a specific application area and guide users in their investigations. Using linked views and other interaction concepts, these tools are required to combine all data domains using meaningful and easy to understand visualization techniques. Especially for the analysis of spatial and temporal data in dynamic processes (e.g., materials tested under load or in different environmental conditions) or multimodal, multiscale data, these tools and techniques are highly anticipated. Only integrated analysis concepts allow to make the most out of all the data available.
  • The Quantitative Data Visualization Challenge centers around the design and implementation of tailored visual analysis systems for extracting and analyzing derived data (e.g., computed from extracted features over spatial, temporal or even higher dimensional domains). Therefore, feature extraction and quantification techniques, segmentation techniques, or clustering techniques, are required as prerequisites for the targeted visual analysis. As the quantification may easily end up in 25 or more properties to be computed per feature, clustering techniques allow to distinguish features of interest into feature classes. These feature classes may then be statistically evaluated to visualize the properties of the individual features as well as the properties of the different classes. Information visualization techniques will be of special interest for solving this challenge.
  • The Visual Debugger Challenge is an idea which uses visual analysis to remove errors in the parametrization of a simulation or a data acquisition process. Similarly, to a debugger in computer programming, identifying errors in the code and providing hints to improve, a visual debugger in the domain of visual computing for materials science should show the following characteristics: It should indicate errors and identify wrongly used algorithms in the data analysis. Such a tool should also identify incorrect parameters, which either show no or very limited benefit or even provide erroneous results. Furthermore, it should give directions on how to improve a targeted analysis and suggest suitable algorithms or pipelines for specific tasks.
  • The Interactive Steering Challenge uses visual analysis tools to control a running simulation or an ongoing data acquisition process. Respective tools monitor costly processes and give directions to improve results regarding the respective targets. For example, in the material analysis domain, this could be a system which provides settings for improved data acquisition based on the current image quality achieved: If the image quality does no more fulfill the target requirements, the system influences all degrees of freedom in the data acquisition to enhance image quality. The same holds for the materials simulation domain. Visual analysis can help to steer target material properties in a specific application environment by predicting tendencies of costly simulation runs, e.g., using cheaper surrogate models.

License
  Creative Commons BY 3.0 DE
  Christoph Heinzl, Robert Michael Kirby, Stepan V. Lomov, Guillermo Requena, and Rüdiger Westermann

Classification

  • Computer Graphics / Computer Vision
  • Data Structures / Algorithms / Complexity
  • Modelling / Simulation

Keywords

  • Visual Computing
  • Materials Science
  • Visualization / Visual Analysis
  • Data Structures
  • Interaction

Book exhibition

Books from the participants of the current Seminar 

Book exhibition in the library, ground floor, during the seminar week.

Documentation

In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.

 

Download overview leaflet (PDF).

Publications

Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.

NSF young researcher support