Dieses Bild zeigt Ilja Kröker

Ilja Kröker

Herr Dr.

Wissenschaftlicher Mitarbeiter
Institut für Wasser- und Umweltsystemmodellierung (LS3/SimTech)

Kontakt

+49 711 685 60745

Visitenkarte (VCF)

Pfaffenwaldring 5a
D-70569 Stuttgart
Raum: 1.15

  1. 2022 (submitted)

    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.
  2. 2022

    1. González-Nicolás A, Bilgic D, Kröker I, Mayar A, Trevisan L, Steeb H, u. a. 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, Rybak I. Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes-Darcy flow problems [Internet]. 2022. Verfügbar unter: http://doi.org/10.21203/rs.3.rs-1742793/v1
    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 [Internet]. arXiv; 2022. Verfügbar unter: https://arxiv.org/abs/2204.06043
  3. 2020

    1. Oladyshkin S, Mohammadi F, Kröker I, Nowak W. Bayesian3 active learning for Gaussian process emulator using information theory. Entropy. 2020;22(0890):1–27.
    2. Oladyshkin S, Beckers F, Kroeker I, Mohammadi F, Heredia A, Noack M, u. a. 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. (Computational Methods in Water Resources (CMWR)).
  4. 2019

    1. 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. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S037704271830551X
    2. Köppel M, Franzelin F, Kröker I, Oladyshkin S, Santin G, Wittwar D, u. a. Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. Computational Geosciences. 2019;23(2):339–54.
  5. 2018

    1. Barth A, Kröker I. Finite Volume Methods for Hyperbolic Partial Differential Equations with Spatial Noise. In: Klingenberg C, Westdickenberg M, Herausgeber. Theory, Numerics and Applications of Hyperbolic Problems I. Cham: Springer International Publishing; 2018. S. 125--135. (Klingenberg C, Westdickenberg M, Reihenherausgeber. Theory, Numerics and Applications of Hyperbolic Problems I).
  6. 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, Herausgeber. Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems: FVCA 8, Lille, France, June 2017 [Internet]. Cham: Springer International Publishing; 2017. S. 189--197. (Cancès C, Omnes P, Reihenherausgeber. Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems: FVCA 8, Lille, France, June 2017). Verfügbar unter: 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(4):807--832.
  7. 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. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S0098135416300503
  8. 2014

    1. 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(10):793–817. Verfügbar unter: http://dx.doi.org/10.1002/zamm.201200174
    2. 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.
    3. Köppel M, Kröker I, Rohde C. Stochastic Modeling for Heterogeneous Two-Phase Flow. In: Fuhrmann J, Ohlberger M, Rohde C, Herausgeber. Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects [Internet]. Springer International Publishing; 2014. S. 353--361. (Fuhrmann J, Ohlberger M, Rohde C, Reihenherausgeber. Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects; Bd. 77). Verfügbar unter: http://dx.doi.org/10.1007/978-3-319-05684-5\_34
  9. 2013

    1. Kröker I. Stochastic models for nonlinear convection-dominated flows [Doctoral dissertation]. Universität Stuttgart; 2013.
  10. 2012

    1. Kröker I, Rohde C. Finite volume schemes for hyperbolic balance laws with multiplicative  noise. Appl Numer Math [Internet]. 2012;62(4):441--456. Verfügbar unter: http://dx.doi.org/10.1016/j.apnum.2011.01.011
  11. 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. S. 195--203. (Finite volumes for complex applications. VI. Problems & perspectives. Volume 1, 2; Bd. 4). Verfügbar unter: http://dx.doi.org/10.1007/978-3-642-20671-9\_21
  12. 2008

    1. Kröker I. Finite volume methods for conservation laws with noise. In: Finite volumes for complex applications V. ISTE, London; 2008. S. 527--534. (Finite volumes for complex applications V).

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 2 Multi-Adaptivität verbessert durch Informationstheorie

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