Data-driven surrogate modeling of structural instabilities in electroactive polymers

PN 3-5

Project description

The project will provide a data-integrated multiscale framework for the continuum-mechanical modeling of porous media. A particular focus will be on theory and numerics of variational scale-bridging techniques for diffusion-driven problems in consideration of large deformations. With such a multiscale framework, we aim at the data-based analysis and optimization of porous microstructures with regard to their overall hydro-mechanical properties. One of our major goals is to provide a data-analytics based approach to instability prediction. Instabilities play a crucial role in materials design and arise, for example, in the form of structural instabilities at micro-level. The occurrence of instabilities depends on a number of conditions including material properties, microscopic morphology and overall coupled loading. From a theoretical viewpoint, the investigation of instabilities calls for minimization-type variational principles. These will be numerically implemented in a consistent way into the multiscale framework. Critical instabilities will be revealed by Bloch-Floquet wave analysis. In order to provide efficient and reliable predictions of the associated instability phenomena, we will equip the multiscale formulation with modern tools of machine learning.

Project title Data-driven surrogate modeling of structural instabilities in electroactive polymers
Project leaders Marc-André Keip (Tim Ricken)
Project duration January 2020 - June 2023
Project number PN 3-5

Publications PN 3-5 and PN 3-5 (II)

  1. 2024

    1. A. Krischok, B. Yaraguntappa, and M.-A. Keip, “Fast implicit update schemes for Cahn–Hilliard-type gradient flow in the context of Fourier-spectral methods,” Computer Methods in Applied Mechanics and Engineering, vol. 431, p. 117220, 2024, doi: https://doi.org/10.1016/j.cma.2024.117220.
    2. S. Sriram, E. Polukhov, and M.-A. Keip, “Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle,” International Journal of Solids and Structures, vol. 291, p. 112663, Apr. 2024, doi: 10.1016/j.ijsolstr.2024.112663.
    3. L. Werneck, M. Han, E. Yildiz, M.-A. Keip, M. Sitti, and M. Ortiz, “A simple quantitative model of neuromodulation, Part I : Ion flow neural ion channels,” Journal of the mechanics and physics of solids, vol. 182, no. January, Art. no. January, 2024, doi: 10.1016/j.jmps.2023.105457.

  2. 2023

    1. A. Müller, M. Bischoff, and M.-A. Keip, “Thin cylindrical magnetic nanodots revisited : Variational formulation, accurate solution and phase diagram,” Journal of magnetism and magnetic materials, vol. 586, p. 171095, 2023, doi: 10.1016/j.jmmm.2023.171095.
    2. E. Polukhov, L. Pytel, and M.-A. Keip, “Swelling-induced pattern transformations of periodic hydrogels : from the  wrinkling of internal surfaces to the buckling of thin films,” Journal of the mechanics and physics of solids, vol. 175, no. June, Art. no. June, 2023, doi: 10.1016/j.jmps.2023.105250.

  3. 2022

    1. A. Kanan, E. Polukhov, M.-A. Keip, L. Dorfmann, and M. Kaliske, “Computational material stability analysis in finite thermo-electro-mechanics,” Mechanics research communications, vol. 121, no. April, Art. no. April, 2022, doi: 10.1016/j.mechrescom.2022.103867.

  4. 2021

    1. S. Nirupama Sriram, E. Polukhov, and M.-A. Keip, “Transient stability analysis of composite hydrogel structures based on a minimization-type variational formulation,” International Journal of Solids and Structures, vol. 230–231, p. 111080, 2021, doi: https://doi.org/10.1016/j.ijsolstr.2021.111080.
    2. E. Polukhov and M.-A. Keip, “Multiscale stability analysis of periodic magnetorheological elastomers,” Mechanics of Materials, vol. 159, p. 103699, 2021, doi: https://doi.org/10.1016/j.mechmat.2020.103699.

  5. 2020

    1. L. T. K. Nguyen, M. Rambausek, and M.-A. Keip, “Variational framework for distance-minimizing method in data-driven computational mechanics,” Computer Methods in Applied Mechanics and Engineering, vol. 365, p. 112898, 2020, doi: https://doi.org/10.1016/j.cma.2020.112898.
    2. E. Polukhov and M.-A. Keip, “Computational homogenization of transient chemo-mechanical processes based on a variational minimization principle,” Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 1, Art. no. 1, Jul. 2020, doi: 10.1186/s40323-020-00161-6.

  6. 2019

    1. F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, Apr. 2019, doi: 10.3390/mca24020040.

Data and software publications PN 3-5 and PN 3-5 (II)

  1. L. Werneck, E. Yildiz, M. Han, M.-A. Keip, M. Sitti, and M. Ortiz, “Ion Flow Through Neural Ion Membrane: scripts and data,” 2023. doi: 10.18419/darus-3575.
To the top of the page