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

Publications

  • König, J., Pfeffer, M. and Stoll, M. (2023). Efficient training of Gaussian processes with tensor product structure. arXiv 2312.15305.

  • Pathiraja, S. (2023): L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients. Stochastic Analysis and Applications, published online doi: 0.1080/07362994.2023.2260863

  • Coghi, M., Torstein, N., Nuesken, N., and Reich, S. (2023). Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering, The Annals of Applied Probability, Volume 33, 5693-5752 doi:DOI: 10.1214/23-AAP1957, arXiv:2107.06621

  • Boege, T., Fritze, R., Görgen, C., Hanselman, J., Iglezakis, D., Kastner, L., Koprucki, T., Krause, T. H., Lehrenfeld, C., Polla, S., Reidelbach, M., Riedel, C., Saak, J., Schembera, B., Tabelow, K., & Weber, M. (2023). Research-data management planning in the German mathematical community. European Mathematical Society Magazine. https://doi.org/10.4171/mag/152

  • Engbert, R. and Rabe, M. M. (2023). Tutorial on dynamical modeling of eye movements in reading. PsyArXiv

  • Boys, B., Girolami, M., Pidstrigach, J., Reich, S., Mosca, A., and Akyildiz, O.D. (2023). Tweedie Moment Projected Diffusions For Inverse Problems arXiv 2310.06721

  • Reich, S. (2023): A particle-based Algorithm for Stochastic Optimal Control. arXiv 2311.06906

  • Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Sampling via gradient flows in the space of probability measures arXiv:2310.03597

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S. (2023). Infinite-Dimensional Diffusion Models arXiv 2302.10130

  • M.A. Freitag, J.M. Nicolaus, M. Redmann (2023). Model order reduction methods applied to neural network training. Proceedings in Applied Mathematics and Mechanics, e202300078. https://doi.org/10.1002/pamm.202300078

  • M.A. Freitag, P.Kriz, T. Mach, J. M. Nicolaus (2023). Can one hear the depth of the water? Proceedings in Applied Mathematics and Mechanics, e202300122. https://doi.org/10.1002/pamm.202300122

  • König, J., Freitag, M.A. (2023). Time-Limited Balanced Truncation for Data Assimilation Problems. Journal of Scientific Computing, Volume 97, Number 47 <doi: 10.1007/s10915-023-02358-4>

  • J. König & M.A. Freitag (2023). Time-limited Balanced Truncation within Incremental Four-Dimensional Variational Data Assimilation. Proceedings in Applied Mathematics and Mechanics, e202300019. https://doi.org/10.1002/pamm.202300019

  • Liu, S., Reich, S., and Tong, X.T. (2023). Dropout ensemble Kalman inversion for high dimensional inverse problems arXiv:2308.16784

  • Reich, S. (2024): Data Assimilation: A Dynamic Homotopy-Based Coupling Approach. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Radomska, A. (eds) Stochastic Transport in Upper Ocean Dynamics II. STUOD 2022. Mathematics of Planet Earth, vol 11. Springer, Cham. doi: 10.1007/978-3-031-40094-0_12

  • Pasemann, G. and Beta, C. and Stannat, W. (2023). Stochastic Reaction-Diffusion Systems in Biophysics: Towards a Toolbox for Quantitative Model Evaluation. arXiv:2307.06655

  • Gaudlitz, S. (2023). Non-parametric estimation of the reaction term in semi-linear SPDEs with spatial ergodicity.arXiv:2307.05457

  • G. Blanchard, A. Carpentier, and O. Zadorozhnyi (2023): Moment inequalities for sums of weakly dependent random fields. In: arXiv preprint arXiv:2306.16403

  • Kim, J. W. and Mehta, P. G. (2023): Duality for Nonlinear Filtering II: Optimal Control. IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2023.3279208

  • Kim, J. W. and Mehta, P. G. (2023): Duality for Nonlinear Filtering I: Observability. IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2023.3279206

  • Kim, J. W. and Mehta, P. G. (2023): Variance Decay Property for Filter Stability. arXiv 2305.12850

  • Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: II. convergence of Onsager–Machlup functionals: II. Infinite product measures on Banach spaces. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f82.

  • M. Redmann, M.A. Freitag (2021). Optimization based model order reduction for stochastic systems. Appl. Math. Comput., 398.

  • H. C. Lie, M. Stahn, T.J. Sullivan (2022). Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, Volume 59, Number 13 doi:10.1007/s10092-022-00457-6.

  • M.A. Freitag, S. Reich (2022). Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen. Mitteilungen der Deutschen Mathematiker-VereinigungVerlag, De GruyterSeiten, 108‒112, Band 30.

  • Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Gradient flows for sampling: Mean-field models, Gaussian approximations and affine invariance arXiv:2302.11024

  • Cvetkovic, N. and Lie, H. C. and Bansal, H. and Veroy-Grepl, K. (2023). Choosing observation operators to mitigate model error in Bayesian inverse problems. ArXiv 2301.04863

  • Kim, J.W. and Reich, S. (2023): On forward-backward SDE approaches to continuousßtime minimum variance estimation. arXiv 2304.12727

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang., S. (2023). Infinite-dimensional diffusion models for function spaces arXiv:2302.10130

  • Irwin, B. and Reich, S. (2023). EnKSGD: A class of preconditioned black box optimization and inversion algorithms. arXiv:2303.16494.

  • Mach, T and Freitag, M.A. (2023). Solving the Parametric Eigenvalue Problem by Taylor Series and Chebyshev Expansion arXiv 230212.03661

  • Schwetlick, L. and Reich S. and Engbert R. (2023). Bayesian Dynamical Modeling of Fixational Eye Movements. arXiv:2303.11941.

  • Rabe, M. M., Paape, D., Mertzen, D., Vasishth, S., & Engbert, R. (2023). SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading. arXiv:2303.05221

  • Janák, J. and Reiß, M. (2023). Parameter estimation for the stochastic heat equation with multiplicative noise from local measurements. arXiv:2303.00074v1

  • Dietrich, F., Makeev, A., Kevrekidis, G., Evangelou, N., Bertalan, T., Reich, S., and Kevrekidis, I.G. (2023). Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learningChaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 33, 023121 doi: 10.1063/5.0113632 arXiv:2106.09004

  • Yadav, H., Smith, G., Reich, S., and Vasishth, S. (2023). Number feature distortion modulates cue-based retrieval in reading. Journal of Memory and Language, Vol. 129, 104400 <doi: 10.1016/j.jml.2022.104400> <doi: 10.31234/osf.io/s4c9t>

  • Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2022). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. arXiv: 2207.03954