1. Data assimilation - the collaborative research centre SFB1294
  2. Open Science
  3. Publications

Publications

  • Calvello, E., Reich, S. and Stuart A.M. (2025): Ensemble Kalman methods: A mean field approach. Acta Numerica 34, 123-291 doi:10.1017/S0962492924000060

  • Ertel, S. W. (2025). On the mean field theory of Ensemble Kalman filters for SPDEs. SIAM/ASA Journal on Uncertainty Quantification, 13(3), 891-930. doi:10.1137/24M1658954

     

  • Mach, T., & Freitag, M. A. (2025). Solving the parametric eigenvalue problem by Taylor series and Chebyshev expansion. SIAM Journal on Matrix Analysis and Applications. https://doi.org/10.1137/23M1551961

  • S. Beier, V. Pfeifer, A. Datta, R. Großmann, C. Beta (2025). Deciphering the dual chemotaxis strategy of bacteria in porous mediaarXiv:2503.05286

  • Koenig, J., Pfeffer, M. & Stoll, M. (2025): Efficient training of Gaussian processes with tensor product structure. Comput Optim Appl. https://doi.org/10.1007/s10589-025-00707-7

     

  • Josie König, Elizabeth Qian, Melina A. Freitag (2025). Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariancesarXiv 2506.23892

  • Opper, Manfred & Reich, S. (2025). On a mean-field Pontryagin minimum principle for stochastic optimal controlarXiv:2506.10506

  • Reich, S. (2025) Ensemble Kalman-Bucy filtering for nonlinear model predictive controlarXiv:2503.12474

  • Gottwald, G.A., Liu, S., Marzouk, Y., Reich, S. & Tong, X.T. (2025) Localized diffusion models for high dimensional distributions generationarXiv:2505.04417

  • Bhandari, D., Pidstrigach, J., & Reich, S. (2025). Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks, Foundations of Data Science, 7, 581-616 doi:10.3934/fods.2024040, arXiv:2309.04742

  • Hartung, N., Khatova, A. (2025), Information-theoretic evaluation of covariate distribution models.J Pharmacokin Pharmacodyn, 52:21

  • Schwetlick, L., Reich, S., & Engbert, R. (2025). Bayesian Dynamical Modeling of Fixational Eye Movements. Biological Cybernetics, 119, 13 doi:10.1007/s00422-025-01010-8

  • Engbert, R., Funken, J., & Boll-Avetisyan, N. (2025). Towards Dynamical Modeling of Infants' Looking Times. WIREs Cognitive Science, 16, e70006 doi:10.1002/wcs.70006</span>

  • Oliviero-Durmus, A., Janati, Y., Moulines, E., Pereyra, M. & Reich, S. (2025) Generative modelling meets Bayesian inference: a new paradigm for inverse problems Philosophical Transactions A 383 (2299), 20240334 doi:10.1098/rsta.2024.0334</span>

  • Gottwald, G., Li, F., Marzouk, Y., and Reich, S. (2025). Stable generative modelling using Schrödinger bridges Philosophical Transactions A 383 (2299), 20240332 doi:10.1098/rsta.2024.0332

  • Liu, S., Reich, S., and Tong, X.T. (2025). Dropout ensemble Kalman inversion for high dimensional inverse problems SIAM Journal on Numerical Analysis 63 (2), 685-715 <span>doi:10.1137/23M159860X

  • Kretschmann, R. and Werner, F. (2025). Maximum a posteriori testing in statistical inverse problems.Inverse Problems and Imaging 19 (6): 1268-1301. doi: 10.3934/ipi.2025015

  • Kim, J. W. and Reich, S. (2025): On forward-backward SDE approaches to continuous-time minimum variance estimation. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 115-136. doi: 10.1007/978-3-031-70660-8

  • Reich, S. (2025): A particle-based Algorithm for Stochastic Optimal Control. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 243-268. doi: 10.1007/978-3-031-70660-8