| In situ eddy analysis in a high-resolution ocean climate model J Woodring, M Petersen, A Schmeißer, J Patchett, J Ahrens, H Hagen IEEE transactions on visualization and computer graphics 22 (1), 857-866, 2016 | 80 | 2016 |
| Smooth convolution-based distance functions A Schmeißer, R Wegener, D Hietel, H Hagen Graphical Models 82, 67-76, 2015 | 10 | 2015 |
| Analysis of the fiber laydown quality in spunbond processes with simulation experiments evaluated by blocked neural networks S Gramsch, A Sarishvili, A Schmeißer Advances in Polymer Technology 2020, 1-11, 2020 | 5 | 2020 |
| Visual parameter space analysis for optimizing the quality of industrial nonwovens VS Victor, A Schmeißer, H Leitte, S Gramsch IEEE Computer Graphics and Applications 42 (2), 56-67, 2022 | 3 | 2022 |
| Analysis of the package diameter in winding processes by image analysis and a linear regression model S Gramsch, EG Bell, A Moghiseh, A Schmeißer Journal of Engineered Fibers and Fabrics 17, 15589250211073249, 2022 | 3 | 2022 |
| Simulation of fiber dynamics and fiber-wall contacts for airlay processes S Gramsch, A Schmeißer, R Wegener Progress in Industrial Mathematics at ECMI 2014 18, 993-1000, 2016 | 3 | 2016 |
| Simulation-based setting suggestions for yarn winding units to reduce color variation in knitted fabric A Schmeißer, EG Bell, S Gramsch, R Heidenreich Textile Research Journal 93 (11-12), 2604-2619, 2023 | 2 | 2023 |
| Modeling and simulation along the process chain for filaments and nonwovens W Arne, C Leithäuser, A Schmeißer Proceedings of the 2nd Young Researcher Symposium (YRS), 78-83, 2013 | 2 | 2013 |
| Numerical treatment of fiber–fiber and fiber-obstacle contacts in technical textile manufacturing F Olawsky, M Hering-Bertram, A Schmeißer, N Marheineke Progress in Industrial Mathematics at ECMI 2010, 335-340, 2012 | 2 | 2012 |
| Machine learning framework to predict nonwoven material properties from fiber graph representations D Antweiler, M Harmening, N Marheineke, A Schmeißer, R Wegener, ... Software Impacts 14, 100423, 2022 | 1 | 2022 |
| Graph-based tensile strength approximation of random nonwoven materials by interpretable regression D Antweiler, M Harmening, N Marheineke, A Schmeißer, R Wegener, ... Machine Learning with Applications 8, 100288, 2022 | 1 | 2022 |
| Ensight4Matlab: read, process, and write files in EnSight® Gold format from C++ or MATLAB®. A Schmeißer, D Burkhart, D Linn, J Schnebele, M Ettmüller, S Gramsch, ... J. Open Source Softw. 2 (20), 217, 2017 | 1 | 2017 |
| Contact Modeling Algorithms for Fiber Dynamics Simulations A Schmeißer Verlag Dr. Hut, 2016 | 1 | 2016 |
| Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems VS Victor, M Ettmüller, A Schmeißer, H Leitte, S Gramsch arXiv preprint arXiv:2404.10472, 2024 | | 2024 |
| Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation VS Victor, A Schmeißer, H Leitte, S Gramsch arXiv preprint arXiv:2404.09604, 2024 | | 2024 |
| Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems V Saajan Victor, M Ettmüller, A Schmeißer, H Leitte, S Gramsch arXiv e-prints, arXiv: 2404.10472, 2024 | | 2024 |
| Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation V Saajan Victor, A Schmeißer, H Leitte, S Gramsch arXiv e-prints, arXiv: 2404.09604, 2024 | | 2024 |
| From production process to operation: Digital twins for filtration R Kirsch, A Schmeißer | | 2023 |
| Vom Prozess bis zur Multiphysik: Digitale Zwillinge für die Filtration R Kirsch, A Schmeißer | | 2023 |
| System for generating setting suggestions for cross winders on the basis of a simulation M Wischnowski, D Bücher, S Gramsch, A Schmeißer, L Paul, ... | | 2018 |
Simone GramschFrankfurt University of Applied Sciences, Fraunhofer ITWMEmail verificata su itwm.fraunhofer.de
