Quantitative characterization of complex particle systems via machine learning and stochastic modeling. - Orkun Furat (2022)

Mixed moving average field guided learning for spatio-temporal data. - Imma Valentina Curato; Orkun Furat; Bennet Stroeh (CoRR, 2023)

Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs. - Lukas Fuchs; Orkun Furat; Donal P. Finegan; Jeffery Allen; Francois L. E. Usseglio-Viretta; Bertan Ozdogru; Peter J. Weddle; Kandler Smith; Volker Schmidt (CoRR, 2024)

Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data. - Lukas Fuchs; Tom Kirstein; Christoph Mahr; Orkun Furat; Valentin Baric; Andreas Rosenauer; Lutz Mädler; Volker Schmidt (Mach. Learn. Sci. Technol., 2024)

Determination of droplet size from wide-angle light scattering image data using convolutional neural networks. - Tom Kirstein; Simon Aßmann; Orkun Furat; Stefan Will; Volker Schmidt (Mach. Learn. Sci. Technol., 2024)

Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes. - Orkun Furat; Sabrina Weber; Johannes Schubert; René Rekers; Maximilian Luczak; Erik Glatt; Andreas Wiegmann; Jürgen Janek; Anja Bielefeld; Volker Schmidt (CoRR, 2025)

Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling. - Somayeh Hosseinhashemi; Philipp Rieder; Orkun Furat; Benedikt Prifling; Changlin Wu; Christoph Thon; Volker Schmidt; Carsten Schilde (CoRR, 2025)

Comparative Analysis of Algorithms for the Fitting of Tessellations to 3D Image Data. - Andreas Alpers; Orkun Furat; Christian Jung; Matthias Neumann; Claudia Redenbach; Aigerim Saken; Volker Schmidt (CoRR, 2025)