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. 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.
    2. 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.
    3. 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. 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. 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. 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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