| Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship C Gebhardt, T Trimborn, F Weber, A Bezold, C Broeckmann, M Herty Mechanics of Materials 151, 2020 | 27 | 2020 |
| Portfolio optimization and model predictive control: a kinetic approach T Trimborn, L Pareschi, M Frank Discrete & Continuous Dynamical Systems - B 24 (11), 6209-6238, 2019 | 21 | 2019 |
| Predictive storage strategy for optimal design of hybrid CSP-PV plants with immersion heater P Richter, T Trimborn, L Aldenhoff Solar Energy 218, 237-250, 2021 | 13 | 2021 |
| Sabcemm-a simulator for agent-based computational economic market models T Trimborn, P Otte, S Cramer, M Beikirch, E Pabich, M Frank Computational Economics, 2019 | 13 | 2019 |
| Mean-field control variate methods for kinetic equations with uncertainties and applications to socioeconomic sciences L Pareschi, T Trimborn, M Zanella International Journal for Uncertainty Quantification 12 (1), 2022 | 6 | 2022 |
| Mean-field and kinetic descriptions of neural differential equations M Herty, T Trimborn, G Visconti arXiv preprint arXiv:2001.04294, 2020 | 6 | 2020 |
| Kinetic theory for residual neural networks M Herty, T Trimborn, G Visconti RWTH Aachen, Institut für Geometrie und Praktische Mathematik, 2020 | 5 | 2020 |
| Recent developments in controlled crowd dynamics MK Banda, M Herty, T Trimborn Crowd Dynamics 2, 2019 | 5 | 2019 |
| Mean field limit of a behavioral financial market model T Trimborn, M Frank, S Martin Physica A: Statistical Mechanics and its Applications 505, 613-631, 2018 | 5 | 2018 |
| Spectral methods to study the robustness of residual neural networks with infinite layers T Trimborn, S Gerster, G Visconti Foundations of Data Science, 0-0, 2019 | 4 | 2019 |
| Simulation of stylized facts in agent-based computational economic market models M Beikirch, S Cramer, M Frank, P Otte, E Pabich, T Trimborn arXiv preprint arXiv:1812.02726, 2018 | 4 | 2018 |
| Simulator for agent based computational economic market models (SABCEMM) T Trimborn, P Otte, S Cramer, M Beikirch, E Pabich, M Frank | 4 | 2018 |
| Kinetic modeling of financial market models T Trimborn RWTH Aachen University, 2017 | 3 | 2017 |
| Continuous limits of residual neural networks in case of large input data M Herty, A Thünen, T Trimborn, G Visconti Communications in Applied and Industrial Mathematics 13 (1), 96-120, 2022 | 2 | 2022 |
| Stylized Facts and Agent-Based Modeling S Cramer, T Trimborn arXiv preprint arXiv:1912.02684, 2019 | 2 | 2019 |
| A macroscopic portfolio model: from rational agents to bounded rationality T Trimborn Mathematics and Financial Economics 13, 491-518, 2019 | 2 | 2019 |
| From Disequilibrium Markets to Equilibrium C Lax, T Trimborn arXiv preprint arXiv:1912.09679, 2019 | 1 | 2019 |
| Dataset for simulation of stylized facts in agent-based computational economic market models M Beikirch, PJ Otte, S Cramer, M Frank, T Trimborn, E Pabich Lehrstuhl für Mathematik (CCES), 2018 | 1 | 2018 |
| Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models M Beikirch, S Cramer, M Frank, P Otte, E Pabich, T Trimborn Advances in Complex Systems 23 (06), 2050017, 2020 | | 2020 |
| Novel Insights in the Levy-Levy-Solomon Agent-Based Economic Market Model M Beikirch, T Trimborn International Journal of Modern Physics C, accepted for publication, 2020 | | 2020 |
Martin FrankComputational Science & Mathematical Methods, Steinbuch Center for Computing, Karlsruhe Institute of TechnologyVerified email at kit.edu
