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

Publications

  • 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

  • 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

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

  • 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). 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

  • 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

  • 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

  • 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

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

  • 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.

  • 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

  • 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