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, 42, 354-369. 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: 10.1214/23-AAP1957

  • 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. doi: 10.4171/mag/152

  • Engbert, R. and Rabe, M. M. (2023). Tutorial on dynamical modeling of eye movements in reading. doi: 10.31234/osf.io/dsvmt

     

  • Lopopolo, A. and Rabovsky, M. (2023). Tracking lexical and semantic prediction error underlying the N400 using artificial neural network models of sentence processing. doi: 10.1101/2022.11.14.516396

  • Gaudlitz, S. and Reiß, M. (2023). Estimation for the reaction term in semi-linear SPDEs under small diffusivity. Bernoulli 29(4): 3033-3058 (November 2023). doi:10.3150/22-BEJ1573arXiv:2203.10527

  • Bhandari, D., Pidstrigach, J., and Reich, S. (2023). Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks. arXiv:2309.04742 

  • Spokoiny, V. (2023). Deviation bounds for the norm of a random vector under exponential moment conditions with applications, arXiv:2309.02302

  • Stankewitz, B. and Mücke, N. and Rosasco, L. (2023). From inexact optimization to learning via gradient concentration. Computational Optimization and Applications 84:265-294. arXiv:2106.05397.

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

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

  • Spokoiny, V. (2023). Sharp deviation bounds and concentration phenomenon for the squared norm of a sub-Gaussian vector, arXiv:2305.07885v1

     

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

  • Spokoiny, V. (2023). Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation, SIAM/ASA Journal on Uncertainty Quantification, 11, 1044-1068, arXiv:2204.11038

  • Spokoiny, V. (2023). Inexact Laplace approximation and the use of posterior mean in Bayesian inference, Bayesian Anal., 1-28, doi:10.1214/23-BA1391

  • Spokoiny, V. (2023). Nonlinear regression: finite sample guarantees, arXiv:2305.08193

  • Spokoiny, V. (2023). Mixed Laplace approximation for marginal posterior and Bayesian inference in error-in-operator model, arXiv:2305.09336

  • Riedel, C., Wiepke, A., Jacob, B., Hartmann, N., and Ulrike, L. (2023). Recommendations for Using Data Management Plans in Academic Research Data Management Training. 10. Fachtagung Hochschuldidaktik Informatik (HDI) 2023 - Conference Proceedings, 145–152. doi: 10.5281/zenodo.10255524.

  • 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

  • Beta, C., Edelstein-Keshet, L., Gov, N. and Yochelis, A. (2023). From actin waves to mechanism and back: How theory aids biological understanding. eLife, 12:e87181, doi: 10.7554/eLife.87181

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

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

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

  • König, J. and 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

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

  • Reiß, M., Strauch, C., and Trottner, L. (2023): Change point estimation for a stochastic heat equation. arXiv:2307.10960

  • 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

  • Zöller, G. and Hainzl, S. (2023). Seismicity scenarios for the remaining operating period of the gas field in Groningen, Netherlands. Seismological Research Letters, Vol. 94(2A), 805-812. doi:10.1785/0220220308

  • Pasemann, G., 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

  • Sharma, S., Hainzl, S., and Zöller, G. (2023): Seismicity parameter dependence on mainshock induced co-seismic stress. Geophysical Journal International, Vol. 135(1), 509-517. doi:10.1093/gji/ggad201

  • Maleki Asayesh,B., Hainzl, S., Zöller, G. (2023): Depth‐Dependent Aftershock Trigger Potential Revealed by 3D‐ETAS Modeling. Journal of Geophysical Research, Vol. 128(6), e2023JB026377. doi:10.1029/2023JB026377

  • Hijazi, S., Freitag, M. A., and Landwehr, N. (2023). POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations. Adv. Model. Simul. Eng. Sci. doi: 10.1186/s40323-023-00242-2

  • Altmeyer, R., Cialenco, I. and Pasemann, G. (2023): Parameter estimation for semilinear SPDEs from local measurements. Bernoulli 29(3): 2035-2061. doi:10.3150/22-BEJ1531

  • Cialenco, I. and Kim, H.-J. and Pasemann, G. (2023): Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach. Stoch PDE: Anal Comp doi:10.1007/s40072-022-00285-3

  • 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

  • Ayanbayev, B., Klebanov, I., Lie, H.C., and Sullivan, T.J. (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.

  • Redmann, M. and Freitag, M.A. (2021). Optimization based model order reduction for stochastic systems. Appl. Math. Comput., 398. doi: 10.1016/j.amc.2020.125783

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

  • Freitag, M.A. and Reich, S. (2022). Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen. Mitteilungen der Deutschen Mathematiker-VereinigungVerlag, De GruyterSeiten, 108‒112, Band 30. doi: 10.1515/dmvm-2022-0037

  • 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., Lie, H. C., 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., and 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 learning. Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 33, 023121. doi: 10.1063/5.0113632, arXiv:2106.09004

  • Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2023). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. Phys. Rev. E, 107:025305 doi: 10.1103/PhysRevE.107.025305

  • 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

  • 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