| Well posedness and convergence analysis of the ensemble Kalman inversion D Blömker, C Schillings, P Wacker, S Weissmann Inverse Problems 35 (8), 085007, 2019 | 63 | 2019 |
| Fokker--Planck particle systems for Bayesian inference: Computational approaches S Reich, S Weissmann SIAM/ASA Journal on Uncertainty Quantification 9 (2), 446-482, 2021 | 55 | 2021 |
| On the incorporation of box-constraints for ensemble Kalman inversion NK Chada, C Schillings, S Weissmann Foundations of Data Science 1 (4), 433-456, 2019 | 32 | 2019 |
| Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion S Weissmann, NK Chada, C Schillings, XT Tong Inverse Problems 38 (4), 045009, 2022 | 19 | 2022 |
| Continuous time limit of the stochastic ensemble Kalman inversion: strong convergence analysis D Blömker, C Schillings, P Wacker, S Weissmann SIAM Journal on Numerical Analysis 60 (6), 3181-3215, 2022 | 16 | 2022 |
| 14 Ensemble Kalman filter for neural network-based one-shot inversion PA Guth, C Schillings, S Weissmann Optimization and Control for Partial Differential Equations: Uncertainty …, 2022 | 14 | 2022 |
| Gradient flow structure and convergence analysis of the ensemble Kalman inversion for nonlinear forward models S Weissmann Inverse Problems 38 (10), 105011, 2022 | 10 | 2022 |
| Ensemble Kalman filter for neural network based one-shot inversion PA Guth, C Schillings, S Weissmann arXiv preprint arXiv:2005.02039, 2020 | 10 | 2020 |
| Beyond stationarity: Convergence analysis of stochastic softmax policy gradient methods S Klein, S Weissmann, L Döring arXiv preprint arXiv:2310.02671, 2023 | 8 | 2023 |
| Consistency analysis of bilevel data-driven learning in inverse problems NK Chada, C Schillings, XT Tong, S Weissmann Communications in Mathematical Sciences 20 (1), 123-164, 2022 | 8 | 2022 |
| Almost sure convergence rates of stochastic gradient methods under gradient domination S Weissmann, S Klein, W Azizian, L Döring arXiv preprint arXiv:2405.13592, 2024 | 7 | 2024 |
| Multilevel Optimization for Inverse Problems S Weissmann, A Wilson, J Zech Conference on Learning Theory, 2022, 5489-5524, 2022 | 7 | 2022 |
| Metropolis-adjusted interacting particle sampling B Sprungk, S Weissmann, J Zech arXiv preprint arXiv:2312.13889, 2023 | 5 | 2023 |
| Particle based sampling and optimization methods for inverse problems SL Weissmann PQDT-Global, 2020 | 3 | 2020 |
| Structure Matters: Dynamic Policy Gradient S Klein, X Zhang, T Başar, S Weissmann, L Döring arXiv preprint arXiv:2411.04913, 2024 | 2 | 2024 |
| Adaptive multilevel subset simulation with selective refinement D Elfverson, R Scheichl, S Weissmann, FA Diaz De La O SIAM/ASA Journal on Uncertainty Quantification 12 (3), 932-963, 2024 | 2 | 2024 |
| On the ensemble Kalman inversion under inequality constraints M Hanu, S Weissmann Inverse Problems 40 (9), 095009, 2024 | 2 | 2024 |
| One-shot learning of surrogates in PDE-constrained optimization under uncertainty PA Guth, C Schillings, S Weissmann SIAM/ASA Journal on Uncertainty Quantification 12 (2), 614-645, 2024 | 2 | 2024 |
| Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality S Kassing, S Weissmann arXiv preprint arXiv:2410.16849, 2024 | 1 | 2024 |
| The ensemble kalman filter for dynamic inverse problems S Weissmann, NK Chada, XT Tong Information and Inference: A Journal of the IMA 13 (4), iaae030, 2024 | | 2024 |
