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

Publications

  • Moldenhawer, T., Moreno, E., Schindler, D., Flemming, S., Holschneider, M., Huisinga, W., Alonso, S. and Beta, C. (2022). Spontaneous transitions between amoeboid and keratocyte-like modes of migration. Front. Cell Dev. Biol., 10:898351, doi:10.3389/fcell.2022.898351

  • Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2022). Three-component contour dynamics model to simulate and analyze amoeboid cell motility. arXiv:2210.12978

  • Yochelis, A., Flemming, S. and Beta, C. (2022) Versatile Patterns in the Actin Cortex of Motile Cells: Self-Organized Pulses Can Coexist with Macropinocytic Ring-Shaped Waves. Phys. Rev. Lett., 129:088101, doi:10.1103/PhysRevLett.129.088101

  • Schwetlick, L.; Backhaus, D. & Engbert, R. (2022). A dynamical scan-path model for task-dependence during scene viewing. Psychological Review, American Psychological Association (APA),doi: 10.1037/rev0000379

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering II: Optimal Control. arXiv 2208.06587

  • Kim, J. W. and Mehta, P. G. (2022): Duality for Nonlinear Filtering I: Observability. arXiv 2208.06586

  • König, J. and Freitag, M. (2022). Time-limited Balanced Truncation for Data Assimilation Problems. arXiv 2212.07719.

  • Vilk, O., Aghion, E., Avgar, T., Beta, C., Nagel, O., Sabri, A., Sarfati, R., Schwartz, D., Weiss, M., Krapf, D., Nathan, R., Metzler, R. and Assaf, M. (2022) Unravelling the origins of anomalous diffusion: From molecules to migrating storks. Phys. Rev. Research, 4:033055, doi:10.1103/PhysRevResearch.4.033055

  • Lie, H. C. and Rudolf, D. and Sprungk, B. and Sullivan, T. J. (2022). Dimension-independent Markov chain Monte Carlo on the sphere. ArXiv 2112.12185

  • Riedel, C., Geßner, H., Seegebrecht, A., Ayon, S. I., Chowdhury, S. H., Engbert, R. & Lucke, U., (2022). Including Data Management in Research Culture Increases the Reproducibility of Scientific Results. In: Demmler, D., Krupka, D. & Federrath, H. (Hrsg.), INFORMATIK 2022. Gesellschaft für Informatik, Bonn. (S. 1341-1352). DOI: 10.18420/inf2022_114

  • Moreno, E., Grossmann, R., Beta, C. and Alonso S. (2022) From Single to Collective Motion of Social Amoebae: A Computational Study of Interacting Cells. Front. Phys., 9:750187, doi:10.3389/fphy.2021.750187

  • Reich, S. (2022): Data assimilation: A dynamic homotopy-based coupling approach. arXiv 2209.05279

  • Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2023). Efficient derivative-free Bayesian inference for large-scale inverse problems. Inverse Probelms, Vol. 38, 125006 <doi: 10.1088/1361-6420/ac99fa> arXiv:2204.04386

  • Pidstrigach, Jakiw (2022), Score-based generative models detect manifolds In: Advances in Neural Information Processing Systems, Volume 35, arXiv:2206.01018

  • Pidstrigach, Jakiw (2022), Convergence of preconditioned Hamiltonian Monte Carlo on Hilbert spaces, IMA Journal of Numerical Analysis, doi:10.1093/imanum/drac052, arXiv:2011.08578

  • Reich, S. (2022). Frequentist perspective on robust parameter estimation using the ensemble Kalman filter In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Radomska, A. (eds) Stochastic Transport in Upper Ocean Dynamics. STUOD 2021. Mathematics of Planet Earth, vol 10. Springer, Cham. doi: 10.1007/978-3-031-18988-3_15 arXiv:2201.000611

  • Calvello, E., Reich, S. and Stuart A.M.(2022): Ensemble Kalman methods: A mean field approach. arXiv 2209.11371

  • Alqahtani, A., Mach, T., and Reichel, L. (2023). Solution of Ill-posed Problems with Chebfun. Numerical Algorithms (2023). doi:10.1007/s11075-022-01390-z arXiv 2007.16137

  • M. Boether, O. Kißig, M. Taraz, S. Cohen, K. Seidel, and T. Friedrich. Whats Wrong with Deep Learning in Tree Search for Combinatorial Optimization. In: International Conference on Learning Representations, 2022.

  • Pidstrigach, J. and Reich, S. (2022). Affine-invariant ensemble transform methods for logistic regression. Foundation of Computational Mathematics, 22, doi:10.10007/s10208-022-09550-2.

  • Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2022): GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. Statistics and Computation, Vol. 32, 29. doi:10.1007/s11222-022-10085-3.

  • Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. arXiv:2204.04386.

  • Yadav, H., Smith, G., Reich, S., and Vasishth, S. (2022). Number feature distortion modulates cue-based retrieval in reading. doi:10.31234/osf.io/s4c9t.

  • Engbert, R., Rabe, M. M., Schwetlick, L., Seelig, S. A., Reich, S., Vasishth, S. (2022). Data assimilation in dynamical cognitive science. Trends in Cognitive Sciences, 26(2), 99-102, doi:10.1016/j.tics.2021.11.006.

  • Malem-Shinitski, N., Ojeda, C., and Opper, M. (2022). Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy, 24(3), 356, doi: 10.3390/e24030356.

  • Mach, T., Reichel, L., and Van Barel, M. (2023). Adaptive cross approximation for Tikhonov regularization in general form. Numerical Algorithms, doi:10.1007/s11075-022-01395-8 arXiv 2204.05740

  • Gaudlitz, S. and Reiß, M. (2022). Estimation for the reaction term in semi-linear SPDEs under small diffusivity. arXiv:2203.10527

  • Pathiraja, S., & van Leeuwen, P. J. (2022). Multiplicative non-Gaussian model error estimation in data assimilation. Journal of Advances in Modeling Earth Systems, 14, e2021MS002564. https://doi.org/10.1029/2021MS002564

  • Ruchi, S., Dubinkina, S. and de Wiljes, J. (2021). Fast hybrid tempered ensemble transform filter for Bayesian elliptical problems via Sinkhorn approximation. Nonlinear Processes in Geophysics, 28(1): 23-41 [1]

  • Reich, S. (2022). Frequentist perspective on robust parameter estimation using the ensemble Kalman filter arXiv:2201.000611