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Towards Simulation-Data Science : A Case Study on Material Failures

Trittenbach, Holger; Gauch, Martin; Böhm, Klemens; Schulz, Katrin

Abstract:
Simulations let scientists study properties of complex systems. At first sight, data mining is a good choice when evaluating large numbers of simulations. But it is currently unclear whether there are general principles that might guide the deployment of respective methods to simulation data. In other words, is it worthwhile to target at “simulation-data science” as a distinct subdiscipline of data science? To identify a respective research agenda and to structure the research questions, we conduct a case study from the domain of materials science. One insight that simulation data may be different from other data regarding its structure and quality, which entails focal points different from the ones of conventional data-analysis projects. It also turns out that interpretability and usability are important notions in our context as well. More attention is needed to gather the various meanings of these terms to align them with the needs and priorities of domain scientists. Finally, we propose extensions to our case study which we deem necessary to generalize our insights towards the guidelines envisioned for “simulation-data science”.

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Volltext §
DOI: 10.5445/IR/1000079420
Veröffentlicht am 25.01.2018
Coverbild
Zugehörige Institution(en) am KIT Fakultät für Maschinenbau (MACH)
Institut für Programmstrukturen und Datenorganisation (IPD)
Institut für Angewandte Materialien - Computational Materials Science (IAM-CMS)
Publikationstyp Forschungsbericht
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2190-4782
urn:nbn:de:swb:90-794207
KITopen-ID: 1000079420
Verlag KIT, Karlsruhe
Umfang 24 S.
Serie Karlsruhe Reports in Informatics ; 2018,1
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