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@PHDTHESIS{Danesh:1023683,
author = {Danesh, Hooman},
othercontributors = {Reese, Stefanie and Cueto, Elías},
title = {{C}omputational homogenization and data-driven surrogate
modeling of mechanical metamaterials},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-10667},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Mechanical metamaterials provide unique opportunities to
tailor material properties for specific engineering
applications. However, their practical use is limited by the
challenges of modeling their complex and multiscale
behavior. Traditional homogenization methods are
computationally expensive, limiting the efficiency of
simulation, prediction, and design processes. This thesis is
driven by the need to create efficient, accurate, and
user-friendly computational tools that integrate
physics-based homogenization with data-driven approaches to
enable rapid property prediction, inverse design, and
uncertainty quantification, ultimately promoting the
widespread use of metamaterials in engineering design. The
present thesis develops an integrated framework that
enhances computational efficiency and accessibility in
metamaterial design. It introduces a two-scale
homogenization approach using truss elements to model
elastoplastic truss-based lattices, significantly reducing
computational costs while maintaining accuracy.
Additionally, it leverages fast Fourier transform
(FFT)-based homogenization to efficiently generate datasets
for metamaterial unit cells, enabling data-driven surrogate
modeling. With their real-time predictive capabilities, the
framework integrates the established surrogate models into a
user-friendly graphical interface that supports property
prediction and inverse design of auxetic metamaterials,
making exploration and design accessible to both experts and
non-experts. Finally, transitioning from deterministic to
probabilistic machine learning approaches, the thesis
establishes uncertainty-aware and data-efficient
reduced-order structure-property linkages for stochastic
metamaterials. This cumulative thesis compiles the
publications of the author (and his co-authors) on
physics-based computational homogenization and data-driven
surrogate modeling of various metamaterial classes,
including truss-based, auxetic, and stochastic structures.
Following an exploration of the motivation, a review of the
current state of the art, and addressing open gaps and
research questions, four published articles are presented.
The first article investigates the limitations of
computational homogenization approaches, particularly $FE^2$
and FFT-based methods, addressing issues such as the lack of
scale separation in 3D-printed structures and infinite
stiffness contrasts that impede conventional FFT solvers.
The second article develops a nonlinear two-scale
homogenization framework for elastoplastic truss-based
lattices, utilizing truss elements at the microscale with
combined nonlinear exponential isotropic and linear
kinematic hardening laws, accurately capturing elastic and
plastic responses across various loading conditions,
validated against direct numerical simulations. The third
article focuses on data-driven surrogate modeling for
auxetic metamaterials with orthogonal voids, using FFT-based
homogenization for efficient dataset generation and training
surrogate models to predict elastic properties in real time
while supporting inverse design through an intuitive
graphical interface. The fourth article explores
probabilistic machine learning for stochastic metamaterials,
employing statistical correlation functions and principal
component analysis (PCA) for dimensionality reduction,
Gaussian process regression (GPR) for robust property
prediction with uncertainty quantification, and an active
learning strategy to minimize training data, enhancing
efficiency for random unit cell designs.},
cin = {311510},
ddc = {624},
cid = {$I:(DE-82)311510_20140620$},
pnm = {XS-Meta - Cross-scale concurrent material-structure design
using functionally-graded 3D-printed matematerials.
(956401)},
pid = {G:(EU-Grant)956401},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-10667},
url = {https://publications.rwth-aachen.de/record/1023683},
}
Gast ::