Materials Map

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

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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.

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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.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2018MULAN: Evaluation and ensemble statistical inference for functional connectivity17citations

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Chart of shared publication
Benar, Christian
1 / 1 shared
Chauvel, Patrick
1 / 2 shared
Bernard, Christophe
1 / 2 shared
Friston, Karl
1 / 1 shared
Jirsa, Viktor K.
1 / 3 shared
Wang, Huifang
1 / 1 shared
Chart of publication period
2018

Co-Authors (by relevance)

  • Benar, Christian
  • Chauvel, Patrick
  • Bernard, Christophe
  • Friston, Karl
  • Jirsa, Viktor K.
  • Wang, Huifang
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document

MULAN: Evaluation and ensemble statistical inference for functional connectivity

  • Woodman, Marmaduke
  • Benar, Christian
  • Chauvel, Patrick
  • Bernard, Christophe
  • Friston, Karl
  • Jirsa, Viktor K.
  • Wang, Huifang
Abstract

A stochastic approach to model crack propagation in random heterogeneous media, using mesoscopic representations of elastic and fracture properties, is presented. In order to obtain reference results, MonteCarlo simulations are first conducted on microstructural samples in which a preexisting crack is propagated by means of a phasefield approach. These computations are used to estimate the subscale?induced randomness on the macroscopic response of the domain. Mesoscopic descriptors are then introduced to investigate scale transition. Elasticity tensor random fields are specifically defined, at that stage, through a moving window upscaling approach. The mesoscopic fracture toughness, which is assumed homogeneous and deterministic, is identified by solving an inverse problem involving the macroscopic peak force. A stochastic model is subsequently constructed in which the mesoscopic elasticity is described as a non?Gaussian random field. This model allows the multiscale?informed elastic counterpart in the phase?field formulation to be sampled without resorting to computational homogenization. The results obtained with the sample?based and model?based mesoscopic descriptions are finally compared with those corresponding to the full?scale microscopic model. It is shown, in particular, that the mesoscopic elasticity?phase?field formulation associated with statically uniform boundary conditions enables the accurate predictions of the mean elastic response and mean peak force.

Topics
  • impedance spectroscopy
  • phase
  • simulation
  • crack
  • elasticity
  • random
  • fracture toughness
  • homogenization