Contact
Pfaffenwaldring 5a
70569 Stuttgart
Room: 1.15
2025
- 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(3):21--54.
2024
- 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. (Geophys. Res. Abstr.; vols. 25, EGU2024-7820).
2023
- 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(3):1–21.
- 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.
- 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
- 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(1):2256774.
- Bürkner PC, 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.
2022
- 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.
- 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.
2020
- 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. (Computational Methods in Water Resources (CMWR)).
- Oladyshkin S, Mohammadi F, Kröker I, Nowak W. Bayesian3 active learning for Gaussian process emulator using information theory. Entropy. 2020;22(0890):1–27.
2019
- 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(2):339–54.
- 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
2018
- 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. p. 125--135. (Klingenberg C, Westdickenberg M, editors. Theory, Numerics and Applications of Hyperbolic Problems I).
2017
- 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. p. 189--197. (Cancès C, Omnes P, editors. Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems: FVCA 8, Lille, France, June 2017). Available from: http://dx.doi.org/10.1007/978-3-319-57394-6_21
- 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.
2016
- 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
2014
- 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.
- 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. Available from: http://dx.doi.org/10.1002/zamm.201200174
- 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. p. 353--361. (Fuhrmann J, Ohlberger M, Rohde C, editors. Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects; vol. 77). Available from: http://dx.doi.org/10.1007/978-3-319-05684-5\_34
2013
- Kröker I. Stochastic models for nonlinear convection-dominated flows [Doctoral dissertation]. Universität Stuttgart; 2013.
2012
- Kröker I, Rohde C. Finite volume schemes for hyperbolic balance laws with multiplicative noise. Appl Numer Math [Internet]. 2012;62(4):441--456. Available from: http://dx.doi.org/10.1016/j.apnum.2011.01.011
2011
- 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. p. 195--203. (Finite volumes for complex applications. VI. Problems & perspectives. Volume 1, 2; vol. 4). Available from: http://dx.doi.org/10.1007/978-3-642-20671-9\_21
2008
- Kröker I. Finite volume methods for conservation laws with noise. In: Finite volumes for complex applications V. ISTE, London; 2008. p. 527--534. (Finite volumes for complex applications V).
10/2007 Diploma in Mathematics, Bielefeld University
10/2013 PhD, Fakulty of Mathematics and Physics, Excellence Cluster Simulation
Technology (SimTech), University of Stuttgart
10/2013-09/2017 PostDoc, Department of Computational Methods for Uncertainty
Quantification, Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
10/2017-03/2018 Professor Substitute for Applied Mathematics,
Friedrich-Alexander-University Erlangen-Nürnberg
04/2018-03/2019 PostDoc, Department of Computational Methods for Uncertainty
Quantification, Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
Since 09/2019 Scientific Assistant, Institute for Modelling Hydraulic and
Environmental Systems, University of Stuttgart
Surrogate-based active learning for parameter inference in geosciences via Bayesian sparse multi-adaptivity enhanced by information theory