Orkun Furat

Scopus Publications

Stereological reconstructions of 3D cellular microstructures by combining adversarial learning and Voronoi tessellations
Lukas Fuchs, Thomas Wilhelm, Orkun Furat, Volker Schmidt
Scientific Reports, 2026
A novel stereological framework to generate synthetic three-dimensional cellular material structures using Voronoi tessellations is presented. While conventional investigations of microstructural features rely on costly and often destructive three-dimensional imaging techniques, our method enables the reconstruction of 3D cellular structures from two-dimensional planar-sectional image data. By representing 3D cell architectures through Voronoi tessellations, we obtain an analytical representation requiring only three parameters per cell, ensuring efficient storage and computational processing. Our framework employs a differentiable approximation of Voronoi tessellations combined with a discriminator neural network in an adversarial learning context, enabling gradient-based optimization of tessellation parameters to generate random 3D cellular structures with statistically similar 2D planar sections as observed in measured 2D image data. We demonstrate the framework on image data of various cellular materials including metallic alloys, biological cells, and foam structures. The presented framework shows state-of-the-art capability of stereologically reconstructing 3D cellular microstructures, while introducing a low-parameter representation, preserving physical interpretability, and ensuring computational efficiency.
Real-time prediction of aggregate structures based on bivariate aerosol dynamics
Lukas Fuchs, Jonah V. Weidemann, Ivan Skenderović, Danijel Čuturić, Orkun Furat, Steven X. Ding, F. Einar Kruis, Volker Schmidt
Chemical Engineering Science, 2026
• Optimized bivariate Monte Carlo simulations enable efficient aggregate descriptor prediction • SPSD attainment in volume and surface area depends on characteristic time scales • CNN surrogate model accelerates prediction, reducing computation time 15 000-fold • Differentiable surrogate model enables efficient reactor temperature optimization Precise control over nanoparticle synthesis in gas-phase processes such as flame and plasma reactors remains a significant challenge because of the complex, non-linear particle formation dynamics governed by coagulation and sintering. This paper presents a computational methodology that combines a Monte Carlo (MC) simulation framework and a convolutional neural network (CNN)-based surrogate model to accelerate predictions of bivariate particle descriptor vector distributions. The MC framework, optimized for computational efficiency, predicts the evolution of the particle surface area and volume distributions over time under isothermal conditions. This bivariate description enables accurate representation of particle morphology, which in turn influences formation dynamics and final product performance. Evaluation against established models demonstrates high agreement, emphasizing its precision in capturing particle formation dynamics. Indications and restrictions are identified for the achievement of a self-preserving size distribution (SPSD) for both aggregate volume and surface area, offering the potential to simplify and facilitate bivariate modeling approaches. The CNN-based surrogate model leverages bivariate histograms to predict time-dependent distributions for variable temperatures, achieving a 15 000-fold reduction in computation time compared to the MC framework and thus reaching real-time capability, while maintaining sufficient accuracy. In addition, the differentiable nature of the model enables the optimization of temperature profiles. This paper demonstrates the potential for integrating advanced MC frameworks with neural networks to balance computational efficiency and predictive accuracy.
Statistical Analysis and Modeling of the 3D Morphology and Texture of Lunar Regolith Simulants
Matthias Weber, Ralf Ditscherlein, Lisa Ditscherlein, Tehya Birch, Markus Franz, Achim Seidel, Urs A Peuker, Orkun Furat, Volker Schmidt, Georg Pöhle
Microscopy and Microanalysis, 2026
Understanding the mechanical behavior of lunar regolith under low-g conditions is essential for processing regolith in the lunar environment. While well understood for many granular materials on Earth, these properties have yet to be studied for lunar regolith. For ground-based experimental investigation of regolith properties, simulants are used, which mimic certain physical or chemical aspects of lunar regolith. However, rheology is significantly influenced by particle size and shape, which has not yet been thoroughly characterized for lunar regolith particles. Moreover, it remains unclear how well common simulants approximate the morphology of lunar regolith particles. In this paper, we quantify the multivariate distributions of size and shape descriptors of actual lunar regolith particles and seven commonly used mare and highlands regolith simulants, using 3D tomographic image data obtained via micro computed tomography. Quantitative analysis confirms that there are large differences in morphology within regolith simulants and between simulants and lunar regolith. This highlights the need to develop regolith simulants with accurate morphologies for experimental investigations of mechanical properties. Alternatively, statistically representative digital models of lunar regolith can be used as input for numerical simulations, enabling simulation studies of morphology-driven mechanical behavior under lunar conditions.
A computational framework for tracking grain boundaries in 3D image data: Quantifying boundary curvatures and velocities in polycrystalline materials
Thomas Wilhelm, Orkun Furat, Jules M. Dake, Carl E. Krill III, Volker Schmidt
Computational Materials Science, 2026
Diffraction-based methods for 3D microstructure mapping have revolutionized the investigation of grain growth phenomena in polycrystalline materials. These techniques provide unprecedented experimental access to the location, shape and migration of individual grain boundaries in real samples, together with other relevant boundary parameters, such as misorientation and inclination. However, achieving the ultimate goal of such studies — the identification of physical mechanisms responsible for the observed evolution of microstructure — is impeded by the voxel-based representation of microstructure intrinsic to 3D mapping. The staircase-like discretization of grain boundaries complicates the determination of boundary positions and local curvatures, motivating a transition from a discrete to a continuous representation of the boundary network. This paper introduces an approach based on smoothing thin-plate splines with radial basis functions to represent grain boundaries as continuous surfaces. The method can approximate arbitrarily shaped boundaries while preserving local mean curvatures and, for time-resolved series of images, local boundary velocities. Its accuracy is validated against two datasets: a multiplicatively weighted Laguerre tessellation, where mean curvatures are known analytically, and a Reuleaux tetrahedron evolving under a phase field model, for which local boundary velocities can be determined exactly. The applicability of the method to experimental data is demonstrated using time-resolved 3D maps of grain growth in an Al–Mg alloy.
Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing
Lukas Fuchs, Philipp Rieder, Donal P. Finegan, Francois Usseglio-Viretta, Jeffery Allen, Melissa Popeil, Orkun Furat, Volker Schmidt
Batteries, 2026
The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.
Assessing the Mixing Quality of Hetero-Aggregates: Applying Mixing Theory to STEM-EDX Elemental Maps
Simon Buchheiser, Orkun Furat, Volker Schmidt, Hermann Nirschl, Frank Rhein
Particle and Particle Systems Characterization, 2026
The hetero‐aggregation of carbon black and colloidal silica in a spray flame is a promising approach to improve dispersibility and stability of carbon black. The mixing quality, i.e., homogeneity of the materials within the hetero‐aggregate, is important for both properties, yet challenging to quantify. For this purpose, multiple STEM‐EDX (Scanning Transmission Electron Microscopy with Energy Dispersive X‐Ray Spectroscopy) elemental maps are conducted on two distinct process conditions, leading to hetero‐aggregates two different mixing qualities. By sampling cutouts of the images at randomized locations and applying principles adapted from classical mixing theory, hetero‐aggregates are characterized with respect to mixing quality, irrespective of composition and primary particle size. Furthermore, correlation coefficient functions give insight to the length scale of primary particle clusters in the hetero‐aggregates. In addition, on the basis of the intra‐aggregate mixing state and the distribution of hetero‐aggregate composition, a suitable description of the inter‐aggregate mixing state is achieved. The developed methods present a generally valid, precise characterization of the mixing quality of hetero‐aggregates.
Stochastic modeling of particle structures in spray fluidized bed agglomeration using methods from machine learning
Lukas Fuchs, Sabrina Weber, Jialin Men, Niklas Eiermann, Orkun Furat, Andreas Bück, Volker Schmidt
Powder Technology, 2026
Agglomeration is an industrially relevant process for the production of bulk materials in which the product properties depend on the morphology of the agglomerates, e.g., on the distribution of size and shape descriptors. Thus, accurate characterization and control of agglomerate morphologies is essential to ensure high and consistent product quality. This paper presents a pipeline for image-based inline agglomerate characterization and prediction of their time-dependent multivariate morphology distributions within a spray fluidized bed process with transparent glass beads. The framework classifies observed objects in image data into three distinct morphological classes–primary particles, chain-like agglomerates and raspberry-like agglomerates–using various size and shape descriptors. Therefore, a fast and robust random forest classifier is trained. Additionally, the fraction of primary particles belonging to each of these classes, either as individual primary particles or as part of a larger structure in the form of chain-like or raspberry-like agglomerates, is described using parametric regression functions. Finally, the temporal evolution of bivariate size and shape descriptor distributions of these classes is modeled using low-parametric regression functions and Archimedean copulas. This approach improves the understanding of agglomerate formation and allows the prediction of process kinetics, facilitating precise control over class fractions and morphology distributions. • Rapid and robust random forest classification for real-time agglomerate categorization. • Multivariate modeling of agglomerate descriptors tailored to individual classes. • Time-series prediction of agglomerate descriptor trends.
Generative Adversarial Framework to Calibrate Excursion Set Models for the 3D Morphology of All-Solid-State Battery Cathodes
Orkun Furat, Sabrina Weber, Anina Dufter, Johannes Schubert, René Rekers, Maximilian Luczak, Erik Glatt, Andreas Wiegmann, Jürgen Janek, Anja Bielefeld, Volker Schmidt
Advanced Intelligent Systems, 2026
This article presents a computational method for generating virtual 3D morphologies of functional materials using low‐parametric stochastic geometry models, that is, digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, which can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise numerous uninterpretable parameters, making systematic variation of morphologies for virtual materials testing challenging. In contrast, low‐parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating digital twins for the morphology of microstructures in all‐solid‐state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures.
Super-resolving 3D nanostructures using artificially generated image data and spatial transport simulations
Orkun Furat, Phillip Gräfensteiner, Rishabh Saxena, Markus Osenberg, Matthias Neumann, Ingo Manke, Thomas Carraro, Volker Schmidt
Machine Learning Science and Technology, 2025
An approach for deploying stochastic three-dimensional (3D) models to generate microstructural 3D image data for training super-resolution networks is investigated for three different scaling factors α ∈ { 2 , 4 , 8 } . The presented approach addresses the issue of scarcity in training data by training the networks only on artificial image data, generated by means of a stochastic 3D model that produces digital twins of the nanoporous inner structure of active particles in battery cathodes. In addition, the performance of super-resolution networks is investigated when complementing the input data, i.e. low-resolved microstructural 3D image data, with spatially resolved transport simulations. The performance of the trained networks is evaluated based on real tomographic image data, and quantified with respect to various geometric descriptors and effective transport properties. It turned out that the integration of transport simulations into the training of super-resolution networks showed an increase in performance for the scaling factors α ∈ { 2 , 4 } , but a decrease in performance for α = 8. However, training the networks on artificial image data was effective in all cases.
Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data
Philipp Rieder, Orkun Furat, Francois L. E. Usseglio-Viretta, Jeffery Allen, Peter J. Weddle, Donal P. Finegan, Kandler Smith, Volker Schmidt
Npj Computational Materials, 2025
Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles. Cracks within these particles develop during calendaring and cycling, reducing connectivity but increasing reactive surface, making their impact on battery performance complex. Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.
Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks
Lukas Fuchs, Orkun Furat, Donal P. Finegan, Jeffery Allen, Francois L. E. Usseglio-Viretta, Bertan Ozdogru, Peter J. Weddle, Kandler Smith, Volker Schmidt
Communications Materials, 2025
Stereological 3D modeling of nano-scale catalyst particles using TEM projections
Lukas Fuchs, Kerstin Wein, Jens Friedland, Orkun Furat, Robert Güttel, Volker Schmidt
Machine Learning Science and Technology, 2025
Quantitative characterization of hydrophobic agglomeration at different mixing intensities using a copula-based probabilistic modeling approach
Niklas Eiermann, Orkun Furat, Jan Nicklas, Urs A. Peuker, Volker Schmidt
Powder Technology, 2025
The Impact of the Feed Rate and the Binder Concentration on the Morphology of Spray-Dried Alumina–Polymer Nanocomposites
Rahul Mitra, Lukas Fuchs, Orkun Furat, Yannik Sinnwell, Sergiy Antonyuk, Volker Schmidt, Urs Alexander Peuker
Processes, 2025
Computational Workflow for the Characterization of Size, Shape, and Composition of Particles and Their Separation Behavior During Processing
Sabrina Weber, Orkun Furat, Tom Kirstein, Thomas Leißner, Urs A. Peuker, Volker Schmidt
Powders, 2025
A length-scale insensitive cohesive phase-field interface model: Application to concurrent bulk and interface fracture simulation in Lithium-ion battery materials
Wan-Xin Chen, Xiang-Long Peng, Jian-Ying Wu, Orkun Furat, Volker Schmidt, Bai-Xiang Xu
Journal of the Mechanics and Physics of Solids, 2025
Artificial filter cake generation: Digital twins via stochastic 3D modeling based on μ-CT image data
Phillip Gräfensteiner, Erik Löwer, Orkun Furat, Urs A. Peuker, Volker Schmidt
Powder Technology, 2025
Mixed moving average field guided learning for spatio-temporal data
Imma Valentina Curato, Orkun Furat, Lorenzo Proietti, Bennet Ströh
Electronic Journal of Statistics, 2025
Methods of Machine Learning and Spatial Stochastics for Characterizing the 3D Morphology of Battery Materials at Various Length Scales
Orkun Furat, Lukas Fuchs, Donal P. Finegan, Kandler Smith, Volker Schmidt
Advanced Structured Materials, 2025
Progress in the Application of Multidimensional Particle Property Distributions: The Separation Function
Edgar Schach, Thomas Buchwald, Orkun Furat, Florentin Tischer, Alexandra Kaas, Laura Kuger, Matthias Masuhr, Johanna Sygusch, Thomas Wilhelm, Ralf Ditscherlein, Urs Alexander Peuker
Kona Powder and Particle Journal, 2025
Application of Multivariate Tromp Functions for Evaluating the Joint Impact of Particle Size, Shape and Wettability on the Separation of Ultrafine Particles via Flotation
Johanna Sygusch, Thomas Wilhelm, Orkun Furat, Kai Bachmann, Volker Schmidt, Martin Rudolph
Powders, 2024
Virtual reassembling of 3D fragments for the data-driven analysis of fracture mechanisms in multi-component materials
Thomas Wilhelm, Trang Thu Võ, Orkun Furat, Urs Alexander Peuker, Volker Schmidt
Computational Materials Science, 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
Machine Learning Science and Technology, 2024
Cohesive phase-field chemo-mechanical simulations of inter- and trans- granular fractures in polycrystalline NMC cathodes via image-based 3D reconstruction
Wan-Xin Chen, Jeffery M. Allen, Shahed Rezaei, Orkun Furat, Volker Schmidt, Avtar Singh, Peter J. Weddle, Kandler Smith, Bai-Xiang Xu
Journal of Power Sources, 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
Machine Learning Science and Technology, 2024
An AFM-based approach for quantification of guest particle deformation during mechano-fusion
Phillip Gräfensteiner, Judith Friebel, Lisa Ditscherlein, Orkun Furat, Urs A. Peuker, Volker Schmidt
Powder Technology, 2024
Investigating the Multidimensional Separation Behavior of Particles in a Cyclosizer Setting—A Case Study on Calcite, Fluorite and Magnesite
Impc 2024 31st Impc International Mineral Processing Congress, 2024
Investigating the Influence of Particle Size and Shape on Froth Flotation Based Beneficiation of Lithium-Rich Minerals in Slags
Impc 2024 31st Impc International Mineral Processing Congress, 2024
Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas
Orkun Furat, Tom Kirstein, Thomas Leißner, Kai Bachmann, Jens Gutzmer, Urs A. Peuker, Volker Schmidt
Minerals Engineering, 2024
Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology
Orkun Furat, Donal P. Finegan, Zhenzhen Yang, Matthias Neumann, Sangwook Kim, Tanvir R. Tanim, Peter Weddle, Kandler Smith, Volker Schmidt
Energy Storage Materials, 2024
Parametric Stochastic Modeling of Particle Descriptor Vectors for Studying the Influence of Ultrafine Particle Wettability and Morphology on Flotation-Based Separation Behavior
Thomas Wilhelm, Johanna Sygusch, Orkun Furat, Kai Bachmann, Martin Rudolph, Volker Schmidt
Powders, 2023
3D analysis of equally X-ray attenuating mineralogical phases utilizing a correlative tomographic workflow across multiple length scales
Silvan Englisch, Ralf Ditscherlein, Tom Kirstein, Leonard Hansen, Orkun Furat, Dominik Drobek, Thomas Leißner, Benjamin Apeleo Zubiri, Alfred P. Weber, Volker Schmidt, Urs A. Peuker, Erdmann Spiecker
Powder Technology, 2023
Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks
Orkun Furat, Donal P. Finegan, Zhenzhen Yang, Tom Kirstein, Kandler Smith, Volker Schmidt
Npj Computational Materials, 2022
Quantifying the Impact of Charge Rate and Number of Cycles on Structural Degeneration of Li-Ion Battery Electrodes
Orkun Furat, Donal P. Finegan, Zhenzhen Yang, Tanvir R. Tanim, Kandler Smith, Volker Schmidt
Journal of the Electrochemical Society, 2022
PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets
Ralf Ditscherlein, Orkun Furat, Erik Löwer, Raik Mehnert, Robin Trunk, Thomas Leißner, Mathias J. Krause, Volker Schmidt, Urs A. Peuker
Microscopy and Microanalysis, 2022
Efficient Fitting of 3D Tessellations to Curved Polycrystalline Grain Boundaries
Lukas Petrich, Orkun Furat, Mingyan Wang, Carl E. Krill III, Volker Schmidt
Frontiers in Materials, 2021
Artificial generation of representative single Li-ion electrode particle architectures from microscopy data
Orkun Furat, Lukas Petrich, Donal P. Finegan, David Diercks, Francois Usseglio-Viretta, Kandler Smith, Volker Schmidt
Npj Computational Materials, 2021
Quantifying the influence of charge rate and cathode-particle architectures on degradation of Li-ion cells through 3D continuum-level damage models
Jeffery M. Allen, Peter J. Weddle, Ankit Verma, Anudeep Mallarapu, Francois Usseglio-Viretta, Donal P. Finegan, Andrew M. Colclasure, Weijie Mai, Volker Schmidt, Orkun Furat, David Diercks, Tanvir Tanim, Kandler Smith
Journal of Power Sources, 2021
Quantitative assessment of microstructural changes of hydrated cement blends due to leaching and carbonation, based on statistical analysis of image data
Orkun Furat, Andre Baldermann, Claudia Baldermann, Martin Dietzel, Volker Schmidt
Construction and Building Materials, 2021
Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation
Orkun Furat, Donal P. Finegan, David Diercks, Francois Usseglio-Viretta, Kandler Smith, Volker Schmidt
Journal of Power Sources, 2021
Estimation of bivariate probability distributions of nanoparticle characteristics, based on univariate measurements
Orkun Furat, Uwe Frank, Matthias Weber, Simon Wawra, Wolfgang Peukert, Volker Schmidt
Inverse Problems in Science and Engineering, 2021
Multiscale Tomographic Analysis for Micron-Sized Particulate Samples
Ralf Ditscherlein, Orkun Furat, Mathieu de Langlard, Juliana Martins de Souza e Silva, Johanna Sygusch, Martin Rudolph, Thomas Leißner, Volker Schmidt, Urs A. Peuker
Microscopy and Microanalysis, 2020
Stochastic modeling of classifying aerodynamic lenses for separation of airborne particles by material and size
Orkun Furat, Matthias Masuhr, Frank Einar Kruis, Volker Schmidt
Advanced Powder Technology, 2020
AUTOMATED EVALUATION OF CONTACT ANGLES IN A THREE-PHASE SYSTEM OF SELECTIVE AGGLOMERATION IN LIQUIDS
Julia Schreier, Orkun Furat, Murat Cankaya, Volker Schmidt, Ulrich Bröckel
Image Analysis and Stereology, 2020
Mineralogical and microstructural response of hydrated cement blends to leaching
Claudia Baldermann, Andre Baldermann, Orkun Furat, Markus Krüger, Manfred Nachtnebel, Hartmuth Schroettner, Joachim Juhart, Volker Schmidt, Josef Tritthart
Construction and Building Materials, 2019
Statistical 3D analysis and modeling of complex particle systems based on tomographic image data
O. Furat, B. Prifling, D. Westhoff, M. Weber, V. Schmidt
Praktische Metallographie Practical Metallography, 2019
Machine learning techniques for the segmentation of tomographic image data of functional materials
Orkun Furat, Mingyan Wang, Matthias Neumann, Lukas Petrich, Matthias Weber, Carl E. Krill, Volker Schmidt
Frontiers in Materials, 2019
Stochastic Modeling of Multidimensional Particle Properties Using Parametric Copulas
Orkun Furat, Thomas Leißner, Kai Bachmann, Jens Gutzmer, Urs Peuker, Volker Schmidt
Microscopy and Microanalysis, 2019
Description of ore particles from X-Ray Microtomography (XMT) images, supported by scanning electron microscope (SEM)-based image analysis
Orkun Furat, Thomas Leißner, Ralf Ditscherlein, Ondřej Šedivý, Matthias Weber, Kai Bachmann, Jens Gutzmer, Urs Peuker, Volker Schmidt
Microscopy and Microanalysis, 2018
On microstructure-property relationships derived by virtual materials testing with an emphasis on effective conductivity
Matthias Neumann, Orkun Furat, Dzmitry Hlushkou, Ulrich Tallarek, Lorenz Holzer, Volker Schmidt
Communications in Computer and Information Science, 2018

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