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

Publications

  • Kim, J. W. and Reich, S. (2025): On forward-backward SDE approaches to continuous-time minimum variance estimation. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 115-136. doi: 10.1007/978-3-031-70660-8

  • Reich, S. (2025): A particle-based Algorithm for Stochastic Optimal Control. In: Chapron, B., Crisan, D., Holm, D., Mémin, E., Coughlan, J.-L. (eds) Stochastic Transport in Upper Ocean Dynamics III. STUOD 2023. Mathematics of Planet Earth, vol 13. Springer, Cham., pp. 243-268. doi: 10.1007/978-3-031-70660-8

  • 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

  • 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

  • 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

  • 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

  • Riedel, C., Geßner, H., Seegebrecht, A., Ayon, S. I., Chowdhury, S. H., Engbert, R. and 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

  • Maoutsa, D. and Opper, M. (2022). Deterministic particle flows for constraining stochastic nonlinear systems, Phys. Rev. Res., 4, 043035, doi:10.1103/PhysRevResearch.4.043035

  • Lie, H. C. and Stahn, M. and Sullivan, T.J. (2022). Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, Volume 59, Number 13, ArXiv 2103.16506, doi: 10.1007/s10092-022-00457-6.

  • Gaucher, S., Carpentier, A., & Giraud, C. (2022). The price of unfairness in linear bandits with biased feedback. Advances in Neural Information Processing Systems, 35, 18363-18376.

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

  • Winkler, L., Ojeda, C., and Opper, M. (2022). A Score-Based Approach for Training Schrödinger Bridges for Data Modelling, Entropy, 25, 316, doi:10.3390/e25020316

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

  • Pidstrigach, J. (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

  • Pfeifer, V., Beier, S., Alirezaeizanjani, Z., and Beta, C. (2022): Role of the two flagellar stators in swimming motility of Pseudomonas putida. Mbio 13(6) e02182-22, doi: 10.1128/mbio.02182-22.

  • 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

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