Dieses Bild zeigt Ilja Kröker

Ilja Kröker

Herr Dr. rer. nat.

Wissenschaftlicher Mitarbeiter
Institut für Wasser- und Umweltsystemmodellierung
Lehrstuhl für Stochastische Simulation und Sicherheitsforschung für Hydrosysteme, SimTech

Kontakt

+49 711 685 60745

Pfaffenwaldring 5a
70569 Stuttgart
Raum: 1.15

  1. 2025

    1. Kröker I, Brünnette T, Wildt N, Oreamuno MFM, Kohlhaas R, Oladyshkin S, et al. Bayesian3 Active Learning for Regularized Multi-Resolution Arbitrary Polynomial Chaos using Information Theory. International Journal for Uncertainty Quantification. 2025 Jan;15:21–54.
    2. Haslauer C, Kroeker I, Nißler E, Oladyshkin S, Nowak W, Class H, et al. Large Temperatures in Water Distribution Pipes as a Water Quality Threat: Measurements and Modelling. In: Geophys. Res. Abstr. Vienna, Austria: EGU General Assembly 2024; 2025.

  2. 2024

    1. Kröker I, Nißler E, Oladyshkin S, Nowak W, Haslauer C. Data-driven surrogate-based Bayesian model calibration for predicting vadose zone temperatures in drinking water supply pipes. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.

  3. 2023

    1. Oladyshkin S, Praditia T, Kroeker I, Mohammadi F, Nowak W, Otte S. The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory. Neural Networks. 2023;166:85–104.
    2. Kohlhaas R, Kröker I, Oladyshkin S, Nowak W. Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark. Computational Geosciences. 2023;27:1–21.
    3. Bürkner P-C, Kröker I, Oladyshkin S, Nowak W. The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coefficients and global selection of terms. Journal of Computational Physics. 2023;112210.
    4. Kröker I, Oladyshkin S, Rybak I. Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems. Computational Geosciences [Internet]. 2023; Available from: https://rdcu.be/dhL31
    5. Bürger R, Chowell G, Kröker I, Lara-Díaz LY. A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile. Journal of Biological Dynamics. 2023;17:2256774.

  4. 2022

    1. González-Nicolás A, Bilgic D, Kröker I, Mayar A, Trevisan L, Steeb H, et al. Optimal exposure time in Gamma-Ray Attenuation experiments for monitoring time-dependent densities. Transport in Porous Media. 2022;143:463–96.
    2. Kröker I, Oladyshkin S. Arbitrary Multi-Resolution Multi-Wavelet-based Polynomial Chaos Expansion for Data-Driven Uncertainty Quantification. Reliability Engineering & System Safety. 2022;222:108376.

  5. 2020

    1. Oladyshkin S, Beckers F, Kroeker I, Mohammadi F, Heredia A, Noack M, et al. Uncertainty quantification using Bayesian arbitrary polynomial chaos for computationally demanding environmental modelling: conventional, sparse and adaptive strategy. In: Computational Methods in Water Resources (CMWR). 2020.
    2. Oladyshkin S, Mohammadi F, Kröker I, Nowak W. Bayesian3 active learning for Gaussian process emulator using information theory. Entropy. 2020;22:1–27.

  6. 2019

    1. Köppel M, Franzelin F, Kröker I, Oladyshkin S, Santin G, Wittwar D, et al. Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational Geosciences. 2019;23:339–54.
    2. Bürger R, Kröker I. Computational uncertainty quantification for some strongly degenerate parabolic convection–diffusion equations. Journal of Computational and Applied Mathematics [Internet]. 2019;348:490–508. Available from: http://www.sciencedirect.com/science/article/pii/S037704271830551X

  7. 2018

    1. Barth A, Kröker I. Finite Volume Methods for Hyperbolic Partial Differential Equations with Spatial Noise. In: Klingenberg C, Westdickenberg M, editors. Theory, Numerics and Applications of Hyperbolic Problems I. Cham: Springer International Publishing; 2018. pp. 125–35.

  8. 2017

    1. Bürger R, Kröker I. Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected Lighthill-Whitham-Richards Traffic Model. In: Cancès C, Omnes P, editors. Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems: FVCA 8, Lille, France, June 2017 [Internet]. Cham: Springer International Publishing; 2017. pp. 189–97. Available from: http://dx.doi.org/10.1007/978-3-319-57394-6_21
    2. Köppel M, Kröker I, Rohde C. Intrusive uncertainty quantification for hyperbolic-elliptic systems governing two-phase flow in heterogeneous porous media. Comput. Geosci. 2017;21:807–32.

  9. 2016

    1. Barth A, Bürger R, Kröker I, Rohde C. Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach. Computers & Chemical Engineering [Internet]. 2016;89:11–26. Available from: http://www.sciencedirect.com/science/article/pii/S0098135416300503

  10. 2014

    1. Kröker I, Nowak W, Rohde C. A stochastically and spatially adaptive parallel scheme for uncertain and non-linear two-phase flow problems. Computational Geosciences. 2014;19:269–84.
    2. Bürger R, Kröker I, Rohde C. A hybrid stochastic Galerkin method for uncertainty quantification applied to a conservation law modelling a clarifier-thickener unit. ZAMM Z. Angew. Math. Mech. [Internet]. 2014;94:793–817. Available from: http://dx.doi.org/10.1002/zamm.201200174
    3. Köppel M, Kröker I, Rohde C. Stochastic Modeling for Heterogeneous Two-Phase Flow. In: Fuhrmann J, Ohlberger M, Rohde C, editors. Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects [Internet]. Springer International Publishing; 2014. pp. 353–61. (Fuhrmann J, Ohlberger M, Rohde C, editors. Springer Proceedings in Mathematics & Statistics; vol. 77). Available from: http://dx.doi.org/10.1007/978-3-319-05684-5\_34

  11. 2013

    1. Kröker I. Stochastic models for nonlinear convection-dominated flows [Doctoral dissertation]. Universität Stuttgart; 2013.

  12. 2012

    1. Kröker I, Rohde C. Finite volume schemes for hyperbolic balance laws with multiplicative noise. Appl. Numer. Math. [Internet]. 2012;62:441–56. Available from: http://dx.doi.org/10.1016/j.apnum.2011.01.011

  13. 2011

    1. Bürger R, Kröker I, Rohde C. Uncertainty quantification for a clarifier-thickener model with random feed. In: Finite volumes for complex applications. VI. Problems & perspectives. Volume 1, 2 [Internet]. Springer; 2011. pp. 195–203. Available from: http://dx.doi.org/10.1007/978-3-642-20671-9\_21

  14. 2008

    1. Kröker I. Finite volume methods for conservation laws with noise. In: Finite volumes for complex applications V. ISTE, London; 2008. pp. 527–34.

10/2007 Diplom Mathematik, Universität Bielefeld
10/2013 Promotion, Fakultät für Mathematik und Physik, Excellenzcluster Simulation Technology (SimTech), Universität Stuttgart
10/2013-09/2017 Postdoktorand, Arbeitsgruppe Computational Methods for Uncertainty Quantification, Institut für Angewandte Analysis und Numerische Simulation, Universität Stuttgart
10/2017-03/2018 Vertretung der Professur für Angewandte Mathematik, Friedrich-Alexander-Universität Erlangen-Nürnberg
04/2018-03/2019 Postdoktorand, Arbeitsgruppe Computational Methods for Uncertainty Quantification, Institut für Angewandte Analysis und Numerische Simulation, Universität Stuttgart
Seit 09/2019 Wissenschaftlicher Mitarbeiter, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart

Surrogate-basiertes aktives Lernen für Parameter-Inferenz in Geowissenschaften via Bayes'sche sparse Multi-Adaptivität verbessert durch Informationstheorie

Projekt: Influence of Soil Temperature on the Warming of Drinking Water in Water Distribution Pipe Networks – Development of a Soil Model

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