| A consensus-based model for global optimization and its mean-field limit R Pinnau, C Totzeck, O Tse, S Martin Mathematical Models and Methods in Applied Sciences 27 (01), 183-204, 2017 | 155 | 2017 |
| An analytical framework for consensus-based global optimization method JA Carrillo, YP Choi, C Totzeck, O Tse Mathematical Models and Methods in Applied Sciences 28 (06), 1037-1066, 2018 | 131 | 2018 |
| Trends in consensus-based optimization C Totzeck Active Particles, Volume 3: Advances in Theory, Models, and Applications …, 2021 | 43 | 2021 |
| Instantaneous control of interacting particle systems in the mean-field limit M Burger, R Pinnau, C Totzeck, O Tse, A Roth Journal of Computational Physics 405, 109181, 2020 | 41 | 2020 |
| Mean-field optimal control and optimality conditions in the space of probability measures M Burger, R Pinnau, C Totzeck, O Tse SIAM Journal on Control and Optimization 59 (2), 977-1006, 2021 | 40 | 2021 |
| Consensus-Based Global Optimization with Personal Best C Totzeck, MT Wolfram arXiv preprint arXiv:2005.07084, 2020 | 29 | 2020 |
| Consensus-based optimization and ensemble Kalman inversion for global optimization problems with constraints JA Carrillo, C Totzeck, U Vaes Modeling and Simulation for Collective Dynamics, 195-230, 2023 | 21 | 2023 |
| An anisotropic interaction model with collision avoidance C Totzeck arXiv preprint arXiv:1912.04234, 2019 | 19 | 2019 |
| A Numerical Comparison of Consensus‐Based Global Optimization to other Particle‐based Global Optimization Schemes C Totzeck, R Pinnau, S Blauth, S Schotthöfer PAMM 18 (1), e201800291, 2018 | 16 | 2018 |
| Parameter calibration with stochastic gradient descent for interacting particle systems driven by neural networks S Göttlich, C Totzeck Mathematics of Control, Signals, and Systems 34 (1), 185-214, 2022 | 9 | 2022 |
| Space mapping-based receding horizon control for stochastic interacting particle systems: dogs herding sheep C Totzeck, R Pinnau Journal of Mathematics in Industry 10, 1-19, 2020 | 9* | 2020 |
| Controlling a self-organizing system of individuals guided by a few external agents--particle description and mean-field limit M Burger, R Pinnau, A Roth, C Totzeck, O Tse arXiv preprint arXiv:1610.01325, 2016 | 9 | 2016 |
| Consensus-based optimization for multi-objective problems: a multi-swarm approach K Klamroth, M Stiglmayr, C Totzeck Journal of Global Optimization, 1-32, 2024 | 8 | 2024 |
| Ensemble-based gradient inference for particle methods in optimization and sampling C Schillings, C Totzeck, P Wacker SIAM/ASA Journal on Uncertainty Quantification 11 (3), 757-787, 2023 | 8 | 2023 |
| Kinetic based optimization enhanced by genetic dynamics G Albi, F Ferrarese, C Totzeck arXiv preprint arXiv:2306.09199, 2023 | 4 | 2023 |
| Interacting Particles and Optimization R Pinnau, C Totzeck PAMM 18 (1), e201800182, 2018 | 4 | 2018 |
| Time-continuous microscopic pedestrian models: an overview R Korbmacher, A Nicolas, A Tordeux, C Totzeck Crowd Dynamics, Volume 4: Analytics and Human Factors in Crowd Modeling, 55-80, 2023 | 3 | 2023 |
| Optimal control for port-Hamiltonian systems and a new perspective on dynamic network flow problems OT Doganay, K Klamroth, B Lang, M Stiglmayr, C Totzeck arXiv preprint arXiv:2303.15082, 2023 | 3 | 2023 |
| Multi-scale description of pedestrian collective dynamics with port-Hamiltonian systems A Tordeux, C Totzeck arXiv preprint arXiv:2211.06503, 2022 | 3 | 2022 |
| Optimal control for interacting particle systems driven by neural networks S Göttlich, C Totzeck arXiv preprint arXiv:2101.12657, 2021 | 3 | 2021 |
