Oliver Kunc

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Scientific Employee

Room 3.136
Pfaffenwaldring 9
70569 Stuttgart

Tel.: +49 (0) 711 / 685 - 66534

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Research objective

The aim of my research is the development of efficient methods for mechanical homogenization in the context of large deformations. This is important for applications in which component parts or structures consist of mixtures of materials. These include, among others, fiber-reinforced composites, foams, porous materials, and mixtures of polymeres.

The homogenization consists in approximating the effective behavior of a heterogeneous micro-structure. This means to find the function describing the macroscopic response (stress, stiffness) of the microstructure to a prescribed macroscopic deformation. The main practical benefit of efficient computational homogenization is the avoidance of experimental studies.

In this work, as well as throughout the EMMA-Group, integral approaches are chosen. Potentials for gains of efficiency are identified and realized at each step from modeling to implementation. The utilized/developed methods include but are not limited to

  • dimensional reduction via
    • projection
    • exploitation of invariances
  • substitution of data-based methods for physical models
    • artificial neural networks
    • kernel interpolation and approximation
  • efficient sampling of high-dimensional data by means of physically motivated methods
  • efficient and stable FE formulations that are compatible with subsequent reduction methods

Publications

[1] Felix Fritzen and Oliver Kunc. Two-stage data-driven homogenization for nonlinear solids using
a reduced order model
. European Journal of Mechanics - A / Solids 69 (2018), DOI:
10.1016/j.euromechsol.2017.11.007
, 201–220.
[2] Oliver Kunc and Felix Fritzen. Finite Strain Homogenization Using a Reduced Basis and Efficient
Sampling
. Mathematical and Computational Applications 24 (2019), DOI: 10.3390/mca24020056

Open Source Code

Minimum Energy Points on Hyperspheres Sd Matlab/Octave
Concentric Interpolation C++
Reduced Basis Demonstrator Matlab/Octave
siehe auch EMMA-Repository Matlab/Octave/C++


Presentations

2019 15th COMPLAS (Barcelona) Two-scale Homogenization of Nonlinear Solids Undergoing Large Deformations
2019 8th GACM Colloq. on Comput. Mechanics (Kassel) Large strain two-scale simulations on laptop computers
2019 90th GAMM Annual Meeting
(Vienna) - KEYNOTE
Computational finite strain homogenization: reduced basis methods and beyond
2018 Euromech Colloquium 597
(Bad Herrenalb)
Efficient finite strain homogenization
2018 10th European Solid Mechanics Conference (Bologna) Two-scale data-assisted mechanical homogenization
2018 4th GAMM AG Data Workshop
(Lüneburg/Geesthacht)
Data-assisted surrogate modelling of nonlinear solids
2018 IUTAM Symposium MORCOS
(Stuttgart)
Efficient large strain homogenization: reduced bases and high-dimensional interpolation
2018 SimTech Seminar: Model Order Reduction (Stuttgart) Efficient large strain homogenization: reduced bases and high-dimensional interpolation
2018 2nd International SimTech Conference (Stuttgart) Efficient training data generation for reduced basis homogenization
2017 7th GACM Colloq. on Comput. Mechanics (Stuttgart) - Best Poster Award: First Prize Two-scale reduced basis homogenization under large deformations
2017 88th GAMM Annual Meeting
(Weimar)
Reduced basis homogenization for hyperelasticity
2016 Workshop on Order Reduction Methods
(Bad Herrenalb)
Neural network training using reduced basis approximation
2016 Workshop on Model Order Reduction and Machine Learning (Stuttgart) Neural network training using reduced basis approximation

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