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

Publications

  • Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S. (2024). Infinite-Dimensional Diffusion Models. JMLR,Vol. 25, 1-52, arXiv 2302.10130

  • Gottwald, Georg A. & Reich, S. (2024). Localized Schrödinger bridge sampler arXiv:2409.07968

  • Albrecht, J., Opper, M., and Großmann, R. (2024): Inferring Parameter Distributions in Heterogeneous Motile Particle Ensembles: A Likelihood Approach for Second Order Langevin Models. arxiv:2411.08692

  • Datta, A., Beier, S., Pfeifer, V., Großmann, R., and Beta, C. (2024): Bacterial motility in porous media follows an active renewal process with power-law distributed dwell times. arxiv: 2408.02317

  • Riedel, C., Hossen Chowdhury, S., Engbert, R., and Lucke, U. (2024): Perceived Barriers to Open Science among Researchers in Mathematics, Natural Sciences, and Cognitive Sciences. In: Klein, M., Krupka, D., Winter, C., Gergeleit, M., & Martin, L. (Hrsg.), INFORMATIK 2024. Gesellschaft für Informatik, Bonn. (S. 2139-2151). doi: 10.18420/INF2024_186

  • Bhandari, D., Pidstrigach, J., and Reich, S. (2024): Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks, Foundations of Data Science, Foundations of Data Science, published online doi:10.3934/fods.2024040, arXiv:2309.04742

  • Lopopolo, A. and Rabovsky, M. (2024): Tracking lexical and semantic prediction error underlying the N400 using artificial neural network models of sentence processing, Neurobiology of Language, 5 (1): 136–166, doi:10.1162/nol_a_00134, bioRxiv 2022.11.14.516396

  • Castillo, A. M., Shprits, Y. Y., Aseev, N. A., Smirnov, A., Drozdov, A., Cervantes, S., et al. (2024): Can we intercalibrate satellite measurements by means of data assimilation? An attempt on LEO satellites. Space Weather, 22, e2023SW003624. doi: 10.1029/2023SW003624

  • Chen, Y., Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2024): Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. Inverse Problems, doi:10.1088/1361-6420/ad847b, arXiv:2406.17263

  • Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2024): Three-component contour dynamics model to simulate and analyze amoeboid cell motility in two dimensions. PLoS ONE, 19(1):e0297511, doi: 10.1371/journal.pone.0297511

  • Sadhu, R.K., Luciano, M., Xi, W., Martinez-Torres, C., Schröder, M., Blum, C., Tarantola, M., Villa, S., Penic, S., Iglic, A., Beta, C., Steinbock, O., Bodenschatz, E., Ladoux, B., Gabriele S. and Gov, N.S. (2024): A minimal physical model for curvotaxis driven by curved protein complexes at the cell's leading edge. PNAS, 121(12):e2306818121, doi: 10.1073/pnas.2306818121

  • Carere, G. and Lie, H. C. (2024). Generalised rank-constrained approximations of Hilbert-Schmidt operators on separable Hilbert spaces and applications, arXiv 2408.05104

  • Carere, G. and Lie, H. C. (2024). Optimal low-rank approximations of posteriors for linear Gaussian inverse problems on Hilbert spaces, arXiv 2411.01112

  • Winkler, L., Richter, L. and Opper, M. (2024): Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models, Proceedings of the 41st International Conference on Machine Learning, 235, 53017-53038, arXiv:2405.03549

     

     

  • Daems, R., Opper, M., Crevecoeur, G., and Birdal, T. (2024): Variational Inference for SDEs Driven by Fractional Noise, The Twelfth International Conference on Learning Representations, arXiv:2310.12975

     

     

  • Spokoiny, V. (2024). Estimation for SLS models: finite sample guarantees, arXiv:2404.14227

  • Cvetkovic, N. and Lie, H. C. and Bansal, H. and Veroy-Grepl, K. (2024): Choosing observation operators to mitigate model error in Bayesian inverse problems. SIAM/ASA Journal of Uncertainty Quantification 12 (3):723-758. ArXiv 2301.04863, doi: 10.1137/23M1602140

  • Stankewitz, B. (2024): Early stopping for L2-boosting in high-dimensional linear models. Annals of Statistics 52 (2):491-518, arXiv:2210.07850.

  • Lie, H. C. (2024): Bayesian inference of covariate-parameter relationships for population modelling. ArXiv 2407.09640

  • Tiepner, A. and Ziebell, E. (2024): Parameter estimation in hyperbolic linear SPDEs from multiple measurements. arXiv:2407.13461

  • Ziebell, E. (2024): Non-parametric estimation for the stochastic wave equation. arXiv:2404.18823

  • V. Pfeifer, V. Muraveva, and Beta, C. (2024): Flagella and Cell Body Staining of Bacteria with Fluorescent Dyes. In: Cell Motility and Chemotaxis: Methods and Protocols, edited by Carsten Beta and Cristina Martinez-Torres (Springer, 2024), p.79-85.

  • Zöller, G. (2024): Recurrence times of large earthquakes: assimilating the effect of seismic coupling into a renewal model. Bulletin of the Seismological Society of America, Vol. 114(3),1754-1761. doi:10.1785/0120230257

  • R. Großmann et al. (2024): Non-Gaussian Displacements in Active Transport on a Carpet of Motile Cells. Phys. Rev. Lett. 132(8) 088301. doi: 10.1103/PhysRevLett.132.088301

  • Datta, A., Beta, C. and Großmann, R. (2024): The random walk of intermittently self-propelled particles. arXiv:2406.15277 (2024)

  • Albrecht, J. and Reich, S. (2024): Robust parameter estimation for partially observed second-order diffusion processes. arXiv:2406.14738

  • Irwin, B., and Reich, S. (2024): EnKSGD: A class of preconditioned black box optimization and inversion algorithms. SIAM Journal on Scientific Computing, 46, A2101-A2122. doi: 10.1137/23M1561142

  • Quinn, P. D., Landmann, M. S., Davis, T., Freitag, M. A., Gazzola, S., and Dolgov, S. (2024): Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction. Chem. Biomed. Imaging 2024. doi: 10.1021/cbmi.3c00116

  • Kaya, A. and Freitag, M. A. (2024). Low-rank solutions to the stochastic Helmholtz equation. Journal of Computational and Applied Mathematics. doi: 10.1016/j.cam.2024.115925

  • Siobhán Correnty, Melina A. Freitag, Kirk M. Soodhalter (2023). Chebyshev HOPGD with sparse grid sampling for parameterized linear systems. arXiv:2309.14178

  • Lie, H. C. and Rudolf, D. and Sprungk, B. and Sullivan, T. J. (2023). Dimension-independent Markov chain Monte Carlo on the sphere. Scandinavian Journal of Statistics 50 (4):1818-1858. ArXiv: 2112.12185  

  • G. Blanchard, A. Carpentier, and O. Zadorozhnyi (2024): Moment inequalities for sums of weakly dependent random fields. In: Bernoulli 30.3, pp. 2501–2520. doi: 10.3150/23-BEJ1682.

  • Kim, J. W. and Mehta, P. G. (2024): Arrow of Time in Estimation and Control: Duality Theory Beyond the Linear Gaussian Model. arXiv 2405.07650

  • Kim, J. W., Joshi, A. A., and Mehta, P. G. (2024): Backward Map for Filter Stability Analysis. arXiv 2405.01127

  • Kim, J. W., Taghvaei, A., and Mehta, P. G. (2024): Divergence metrics in the study of Markov and hidden Markov processes. arXiv 2404.15779

  • Kim, J. W. and Mehta, P. G. (2024): Variance Decay Property for Filter Stability. IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2024.3413573

  • Janák, J. and Reiß, M. (2024): Parameter estimation for the stochastic heat equation with multiplicative noise from local measurements. To appear in: Stochastic Processes and their Applications doi:10.1016/j.spa.2024.104385

  • Cherepanov, V., and Ertel, S. W. (2024): Neural Networks-based Random Vortex Methods for Modelling Incompressible Flows. arXiv: 2405.13691

  • Tienstra, M. (2024). Early Stopping for Ensemble Kalman-Bucy Inversion. arXiv:2403.18353

  • Ertel, S.E. and Stannat, W. (2024): Analysis of the ensemble Kalman-Bucy filter for correlated observation noise. Ann. Appl. Probab. 34(1B), 1072-1107, doi: 10.1214/23-AAP1985.

  • Haas, B., Shprits, Y. Y., Wutzig, M., Szabó-Roberts, M., García Peñaranda, M., Castillo Tibocha, A. M., Himmelsbach, J., Wang, D., Miyoshi, Y., Kasahara, S., Keika, K., Yokota, S., Shinohara, I., and Hori, T. (2024). Global validation of data-assimilative electron ring current nowcast for space weather applications.Sci Rep 14, 2327. doi: 10.1038/s41598-024-52187-0.

  • Gottwald, G., Li, F., Marzouk, Y., Reich, S (2024). Stable generative modelling using diffusion maps. arXiv 2401.04372