7 citations · 16 across the 20 of their papers we have counts for
6 papers · 1 filter
Physics-informed Neural Operators for Predicting 3D Electromagnetic Fields Transformed by Metasurfaces
Orkun Furat, Vinay Chakravarthi Gogineni, Henrik Bindslev +1
Metasurfaces, typically realized as arrays of nanopillars, transform electromagnetic (EM) fields depending on their geometry and spatial arrangement. For solving the inverse proble…
Fitting Generalized Power Diagrams to 3D Image Data: A Prerequisite for Virtual Materials Testing
Andreas Alpers, Orkun Furat, Christian Jung +4
This paper reviews algorithmic and modeling approaches for fitting generalized power diagrams to three-dimensional image data, a key step in virtual materials testing (VMT). Beyond…
Quantitative characterization of hydrophobic agglomeration at different mixing intensities using a copula-based probabilistic modeling approach
Niklas Eiermann, Orkun Furat, Jan Nicklas +2
The agglomeration of small poorly wetted alumina particles in a stirred tank is investigated. For different experimental conditions, two bivariate probability densities for the are…
Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling
Somayeh Hosseinhashemi, Philipp Rieder, Orkun Furat +5
The 3D microstructure of porous media, such as electrodes in lithium-ion batteries or fiber-based materials, significantly impacts the resulting macroscopic properties, including e…
Stereological 3D modeling of nano-scale catalyst particles using TEM projections
Lukas Fuchs, Kerstin Wein, Jens Friedland +3
Catalysis, particularly heterogeneous catalysis, is crucial in the chemical industry and energy storage. Approximately 80% of all chemical products produced by heterogeneous cataly…
Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes
Orkun Furat, Sabrina Weber, Johannes Schubert +7
This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, cali…