Materials Map

Discover the materials research landscape. Find experts, partners, networks.

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

×

Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Zschocke, Selina

  • Google
  • 2
  • 3
  • 12

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2022A concept for data-driven computational mechanics in the presence of polymorphic uncertain properties12citations
  • 2021Data-driven computational mechanics with polymorphic uncertain datacitations

Places of action

Chart of shared publication
Kaliske, Michael
1 / 16 shared
Graf, Wolfgang
1 / 3 shared
Leichsenring, Ferenc
1 / 2 shared
Chart of publication period
2022
2021

Co-Authors (by relevance)

  • Kaliske, Michael
  • Graf, Wolfgang
  • Leichsenring, Ferenc
OrganizationsLocationPeople

document

Data-driven computational mechanics with polymorphic uncertain data

  • Zschocke, Selina
Abstract

Traditional computational mechanics are based on constitutive models representing materialbehavior. In case of solid mechanics, these result from assuming a class of material behavior and fitting the corresponding parameters to experimentally obtained stress-strain data. In contrast to that, by the method of data-driven computing, numerical simulations can be executed directly based on stress-strain data. Therefore, the step of material modeling is bypassed and the analysis is performed through minimizing the distance between the data set and the subspace of stress-strain states which are compatible in terms of kinematic compatibility and equilibrium. <br/><br/>Uncertainty needs to be taken into account to provide realistic assessments of structural responses. Aleatoric uncertainty, describing variability, and epistemic uncertainty, caused by inaccuracy and incompleteness, are distinguished. The modeling of polymorphic uncertainty, enabling simultaneous consideration of aleatoric and epistemic uncertainty, in the context of structural analysis is a current research field. <br/><br/>The material behavior of composite materials is strongly affected by the heterogeneities occurring as a result of the combination of individual constituents. Based on numerical homogenization methods the problem is divided into different length scales and the mechanical behavior on the mesoscale, representing heterogeneities, is considered within the structural analysis of the homogeneous macroscopic replacement continuum. <br/><br/>In this thesis, a workflow for the quantification of polymorphic uncertain stress-strain databased on different types of available information is established. Based on this, a decoupled numerical homogenization scheme with the purpose of taking mesoscale uncertainties into account utilizing the method of data-driven computing is developed and introduced. In order to demonstrate the workflow and characteristics of the proposed framework, deterministic and uncertain structural analyses are executed.

Topics
  • impedance spectroscopy
  • simulation
  • composite
  • homogenization