Dieses Bild zeigt Sergey Oladyshkin

Sergey Oladyshkin

Herr apl. Prof. Dr.-Ing.

Stellvertretender Abteilungsleiter
Institut für Wasser- und Umweltsystemmodellierung
Lehrstuhl für Stochastische Simulation und Sicherheitsforschung für Hydrosysteme, SimTech
[Foto: SimTech/Max Kovalenko]

Kontakt

+49 711 685 60116

Pfaffenwaldring 5a
70569 Stuttgart
Raum: 2.29

Sprechstunde

Nach Vereinbarung per E-Mail

  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. Morales Oreamuno MF, Menzel N, Oladyshkin S, Wagner FM, Nowak W. Surrogate-assisted Bayesian inference with ERT data for contaminant transport modelling in the subsurface. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2025; 2025.
    3. 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.
    4. Wei R, Le AV, Liu B, Azari M, Nowak W, Kappler A, et al. Modeling the Ammonium Removal Processes in Household Sand Filters. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2025; 2025.

  2. 2024 (submitted)

    1. Kohlhaas R, Hommel J, Weinhardt F, Class H, Oladyshkin S, Flemisch B. Numerical Investigation of Preferential Flow Paths in Enzymatically Induced Calcite Precipitation Supported by Bayesian Model Analysis. Transport in Porous Media.
    2. Köse G, Zamora JDS, Osmancevic E, Janotte F, Oladyshkin S, Nowak W. Bayesian failure localization identifies inconsistencies between water distribution network models and real-world conditions. Journal of Water Resources Planning and Management - ASCE.

  3. 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.
    2. Morales Oreamuno MF, Oladyshkin S, Nowak W. Training surrogate models using input dimension reduction and Bayesian active learning techniques for inverse modelling in heterogeneous media applications. In: geoENV2024 Book of Abstracts. Chania, Crete, GR: Creative Commons Licence BY-NC-ND 4.0; 2024. pp. 191–2.
    3. Chen Q, Boxberg MS, Menzel N, Morales Oreamuno MF, Nowak W, Oladyshkin S, et al. The site selection data hub: a data-centric approach for integrated simulation workflow management in radioactive waste disposal site selection. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.
    4. Wildt N, Oladyshkin S. Learning Kinetic Sorption Mechanisms Using Ordinary Differential Arbitrary Polynomial Chaos Expansion. Tucson, Arizona, USA: 25th International Conference on Computational Methods in Water Resources (CMWR); 2024.
    5. Morales Oreamuno MF, Oladyshkin S, Nowak W. Error-aware surrogate modelling with input dimension reduction for groundwater modelling in heterogenous media. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.
    6. Kurgyis K, Achtziger-Zupančič P, Bjorge M, Boxberg MS, Broggi M, Buchwald J, et al. Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste: Introduction of a research initiative. Environmental Earth Sciences. 2024 Jan;83.

  4. 2023

    1. Köse G, Oladyshkin S, Nowak W. Optimizing Pressure Monitoring in a Water Distribution Network through Bayesian Calibration. In: 18th Pipeline Technology Conference 2023 [Internet]. Berlin, Germany: EITEP Institute; 2023. Available from: https://www.pipeline-conference.com/abstracts/optimizing-pressure-monitoring-water-distribution-network-through-bayesian-calibration
    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. Horuz CC, Karlbauer M, Praditia T, Butz MV, Oladyshkin S, Nowak W, et al. Physical Domain Reconstruction with Finite Volume Neural Networks. Applied Artificial Intelligence. 2023;37:2204261.
    4. 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.
    5. Morales Oreamuno MF, Oladyshkin S, Nowak W. Information-Theoretic Scores for Bayesian Model Selection and Similarity Analysis: Concept and Application to a Groundwater Problem. Water Resources Research. 2023 Jul;59:e2022WR033711.
    6. Mohammadi F, Eggenweiler E, Flemisch B, Oladyshkin S, Rybak I, Schneider M, et al. A surrogate-assisted uncertainty-aware Bayesian validation framework and its application to coupling free flow and porous-medium flow. Computational Geosciences. 2023 Jul;27:663–86.
    7. Schwindt S, Callau Medrano S, Mouris K, Beckers F, Haun S, Nowak W, et al. Bayesian calibration points to misconceptions in three-dimensional hydrodynamic reservoir modelling. Water Resources Research. 2023;59:e2022WR033660.
    8. 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
    9. 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.
    10. Zhang L, Nowak W, Oladyshkin S, Wang Y, Cai J. Opportunities and challenges in CO2 geologic utilization and storage. Advances in Geo-Energy Research. 2023 Jul;8:141–5.
    11. Mouris K, Acuna Espinoza E, Schwindt S, Mohammadi F, Haun S, Wieprecht S, et al. Stability criteria for Bayesian calibration of reservoir sedimentation models. Modeling Earth Systems and Environment [Internet]. 2023; Available from: https://doi.org/10.1007/s40808-023-01712-7

  5. 2022

    1. Cheng K, Z L, Xiao S, Oladyshkin S, Nowak W. Mixed covariance function Kriging model for uncertainty quantification. International Journal for Uncertainty Quantification. 2022;12:17–30.
    2. Horuz CC, Karlbauer M, Praditia T, Butz MV, Oladyshkin S, Nowak W, et al. Inferring Boundary Conditions in Finite Volume Neural Networks. In: Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M, editors. International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2022. Cham: Springer International Publishing; 2022. pp. 538–49.
    3. 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.
    4. Praditia T, Karlbauer M, Otte S, Oladyshkin S, Butz MV, Nowak W. Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Water Resources Research. 2022;58.
    5. Karlbauer M, Praditia T, Otte S, Oladyshkin S, Nowak W, Butz MV. Composing Partial Differential Equations with Physics-Aware Neural Networks. In: Proceedings of the 39th International Conference on Machine Learning. Baltimore, USA; 2022. pp. 10773–801. (Proceedings of Machine Learning Research).

  6. 2021 (submitted)

    1. Cheng K, Lu Z, Xiao S, Oladyshkin S, Nowak W. Unified Bayesian inference framework for surrogate modelling: connection between existing techniques and their common fundamentals. Reliability Engineering and System Safety.

  7. 2021

    1. Praditia T, Oladyshkin S, Nowak W. Finite Volume Neural Networks: a Hybrid Modeling Strategy for Subsurface Contaminant Transport. In: AGU Fall Meeting 2021. 2021.
    2. Praditia T, Karlbauer M, Otte S, Oladyshkin S, Butz M, Nowak W. Finite Volume Neural Network: Modeling Subsurface Contaminant Transport. In: Deep Learning for Simulation ICLR Workshop 2021 [Internet]. 2021. Available from: https://arxiv.org/abs/2104.06010
    3. Cheng K, Xiao S, Zhang X, Oladyshkin S, Nowak W. Resampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing. 2021;156:107630.
    4. Scheurer S, Schäfer Rodrigues Silva A, Mohammadi F, Hommel J, Oladyshkin S, Flemisch B, et al. Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation. Computational Geosciences. 2021;25:1899–917.
    5. Praditia T, Oladyshkin S, Nowak W. Physics Informed Neural Network for porous media modelling. Stuttgart, Germany: InterPore German Chapter Meeting 2021; 2021.
    6. Praditia T, Oladyshkin S, Nowak W. Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow. online: EGU General Assembly 2021; 2021.
    7. Xiao S, Praditia T, Oladyshkin S, Nowak W. Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis. Applied Energy [Internet]. 2021;285:116456. Available from: https://www.sciencedirect.com/science/article/pii/S0306261921000222
    8. Flaig S, Praditia T, Kissinger A, Lang U, Oladyshkin S, Nowak W. Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network. online: EGU General Assembly 2021; 2021.

  8. 2020

    1. Beckers F, Heredia A, Noack M, Nowak W, Wieprecht S, Oladyshkin S. Bayesian Calibration and Validation of a Large-Scale and Time-Demanding Sediment Transport Model. Water Resources Research. 2020;56.
    2. Xiao S, Oladyshkin S, Nowak W. Forward-reverse switch between density-based and regional sensitivity analysis. Applied Mathematical Modelling. 2020;84:377–92.
    3. Xiao S, Oladyshkin S, Nowak W. Reliability analysis with stratified importance sampling based on adaptive Kriging. Reliability Engineering & System Safety. 2020;197:106852.
    4. Manguang G, Zhang L, Miao X, Oladyshkin S, Cheng X, Wang Y, et al. Application of computed tomography (CT) in geologic CO_2 storage research: a critical review. Journal of Natural Gas Science and Engineering. 2020;103591.
    5. 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.
    6. Oladyshkin S, Mohammadi F, Kröker I, Nowak W. Bayesian3 active learning for Gaussian process emulator using information theory. Entropy. 2020;22:1–27.
    7. Guthke A, Bakhshipour AE, de Barros FPJ, Class H, Daniell JE, Dittmer U, et al. A unified framework for quantitative interdisciplinary flood risk assessment. In: AGU. 2020.
    8. Guthke A, Bakhshipour AE, de Barros F, Class H, Daniell JE, Dittmer U, et al. A unified framework for quantitative interdisciplinary flood risk assessment. online: AGU Fall Meeting 2020; 2020.
    9. Praditia T, Walser T, Oladyshkin S, Nowak W. Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies [Internet]. 2020;13:3873. Available from: https://www.mdpi.com/1996-1073/13/15/3873

  9. 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. Xiao S, Oladyshkin S, Nowak W. Reliability sensitivity analysis with subset simulation: application to a carbon dioxide storage problem. In: International Conference of Euro Asia Civil Engineering Forum. Stuttgart, Germany: International Conference of Euro Asia Civil Engineering Forum; 2019.
    3. J. Salgado, Oladyshkin S, Osmancevic E, Janotte F. Kalibrierung von Rechennetzmodellen anhand probabilistischer Bayes‘scher Verfahren. DWGV energie wasser-praxis. 2019;2:16–21.
    4. Xiao S, Reuschen S, Köse G, Oladyshkin S, Nowak W. Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing. 2019;133:106248.
    5. Praditia T, Walser T, Oladyshkin S, Nowak W. Using physics-based regularization in Artificial Neural Networks to predict thermochemical energy storage systems. In: Fall Meeting 2019, Abstract: IN32B-15. San Francisco, CA, USA: American Geophysical Union (AGU); 2019.
    6. Oladyshkin S, Nowak W. The connection between Bayesian Inference and Information Theory for model selection, information gain and experimental design. Entropy. 2019;21:1081.

  10. 2018

    1. Oladyshkin S, Guthke A, Mohamadi F, Kopmann R, Nowak W. Model selection under computational time constraints: application to river engineering. Saint-Malo, France: XXII. International Conference on Computational Methods in Water Resources (CMWR); 2018.
    2. Mohammadi F, Kopmann R, Guthke A, Oladyshkin S, Nowak W. Bayesian selection of hydro-morphodynamic models under computational time constraints. Advances in Water Resources [Internet]. 2018;117:53–64. Available from: http://www.sciencedirect.com/science/article/pii/S0309170817311909
    3. Oladyshkin S, Nowak W. Incomplete statistical information limits the utility of higher-order polynomial chaos expansions. Reliability Engineering & System Safety. 2018;169:137–48.
    4. Guthke A, Oladyshkin S, Mohammadi F, Kopmann R, Nowak W. Bayesian model selection under computational time constraints: application to river modeling. In: Fall Meeting 2018. Washington, D.C., USA: American Geophysical Union (AGU); 2018.

  11. 2017

    1. Namhata A, Zhang L, Dilmore RM, Oladyshkin S, Nakles DV. Modeling Pressure Changes due to Migration of Fluids into the Above Zone Monitoring Interval of a Geologic Carbon Storage Site. International Journal of Greenhouse Gas Control. 2017;56:30–42.
    2. Agada SS, Geiger S, ElSheikh A, Oladyshkin S. Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs. Petroleum Geoscience. 2017;23:270–83.
    3. Fernandez B, Kopmann R, Oladyshkin S. Automated Calibration For Numerical Models Of Riverflow. In: General Assembly 2017, Geophysical Research Abstracts 19: EGU2017-1026-3, 2017. Vienna, Austria: European Geosciences Union (EGU); 2017.

  12. 2016

    1. Oladyshkin S, Nowak W. Incomplete statistical information limits the utility of higher-order polynomial chaos expansions. Lausanne Switzerland: SIAM Conference on Uncertainty Quantification; 2016.
    2. Schulte DO, Rühaak W, Oladyshkin S, Welsch B, Sass I. Optimization of Medium Deep Borehole Thermal Energy Storages. Energy Technology. 2016;104–13.
    3. Namhata A, Oladyshkin S, Dilmore RM, Zhang L, Nakles DV. Probabilistic Assessment of Above Zone Pressure Predictions at a Geologic Carbon Storage Site. Scientific Reports. 2016;6:39536.
    4. T. H. Mai, W. Nowak RK, Oladyshkin S. Uncertainty quantification for a hydro-morphodynamic model of river Rhine. In: General Assembly 2016, Geophysical Research Abstracts 18: EGU2016-5701-3, 2016. Vienna, Austria: European Geosciences Union (EGU); 2016.
    5. Zhang Y, Liu Y, Pau G, Oladyshkin S, Finsterle S. Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses. International Journal of Greenhouse Gas Control. 2016;49:217–26.
    6. Schulte D, Rühaak W, Welsch B, Oladyshkin S, Sass I. Optimization of borehole heat exchanger arrays. Energy Storages. In: General Assembly 2016, Geophysical Research Abstracts 18: EGU2016-8405-1, 2016. Vienna, Austria: European Geosciences Union (EGU); 2016.
    7. Mai TH, Nowak W, Kopmann R, Oladyshkin S. Uncertainty quantification for a hydro-morphodynamic model of river Rhine. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2016; 2016.

  13. 2015

    1. Hlawatsch M, Oladyshkin S, Weiskopf D. Employing Model Reduction for Uncertainty Visualization in the Context of CO$_2$ Storage Simulation. Visualization for Decision Making Under Uncertainty. In: SPE Reservoir Simulation Symposium. Chicago, IL, USA: IEEE VIS 2015 Conference; 2015.
    2. Agada S, Geiger S, Elsheikh AH, Mackay E, Oladyshkin S. Reduced Order Models for Rapid EOR Simulation in Fractured Carbonate Reservoirs. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers; 2015. pp. SPE–173205.
    3. A. Namhata, S. Oladyshkin RD, Nakles DV. Leakage Characterization through Above Zone Monitoring Interval: Uncertainty Quantification and Sensitivity Analysis. Pittsburgh, PA, USA: The 14th Annual Conference on Carbon Capture Utilization and Storage; 2015.
    4. Namhata A, Dilmore R, Oladyshkin S, Zhang L, Nakles DV. Modeling, Uncertainty Quantification and Sensitivity Analysis of Subsurface Fluid Migration in the Above Zone Monitoring Interval of a Geologic Carbon Storage Site. In: Fall Meeting 2015, Abstract: H51U-05. San Francisco, CA, USA: American Geophysical Union (AGU); 2015.

  14. 2014

    1. Oladyshkin S, Schröder P, Class H, Nowak W. Stochastic model calibration for large-scale applications via Bayesian updating combined with arbitrary polynomial chaos. Stuttgart, Germany: XX. International Conference on Computational Methods in Water Resources (CMWR); 2014.
    2. Elsheikh AH, Oladyshkin S, Nowak W, Christie M. Estimating the probability of CO_2 leakage using rare event simulation. In: ECMOR XIV-14th European conference on the mathematics of oil recovery. 2014. p. We B25.
    3. Oladyshkin S, de Barros HCRHWNFPJ, Ashraf M. Data-driven polynomial response surfaces as efficient tool for applied tasks under uncertainty. Research Triangle Park, NC, USA: SAMSI Geosciences Applications Opening Workshop; 2014.
    4. Zhang Y, Oladyshkin S, Y YL, Pau GSH. Comparison of four reduced order models for uncertainty quantification in subsurface flow and transport problems. Stuttgart, Germany: XX. International Conference on Computational Methods in Water Resources (CMWR); 2014.
    5. Karajan N, Otto D, Oladyshkin S, Ehlers M. Application of the polynomial chaos expansion to approximate the homogenised response of the intervertebral disc. Biomechanics and modeling in mechanobiology. 2014;13:1065–80.
    6. Zhang Y, Oladyshkin S, Y YL, Pau GSH. Comparison of Applying four Reduced Order Models to a Global Sensitivity Analysis. In: Fall Meeting 2014, Abstract: H31J-0761. San Francisco, CA, USA: American Geophysical Union (AGU); 2014.
    7. Oladyshkin S, Nowak W. Analyzing the expansion order for polynomial chaos expansions in the light of imprecise information on statistical input distributions. Boulder, CO, USA: 1st International Conference on Frontiers in Computational Physics: Modeling the Earth System; 2014.
    8. Franzelin F, Pflüger D, Oladyshkin S. Uncertainty quantification with adaptive sparse grids and its application to CO$_2$ storage. Stuttgart, Germany: XX. International Conference on Computational Methods in Water Resources (CMWR); 2014.
    9. Namhata A, Oladyshkin S, Dilmore R, Nakles DV. Leakage Characterization through Above Zone Monitoring Interval: Uncertainty Quantification and Sensitivity Analysis. Pittsburgh, PA, USA: The 14th Annual Conference on Carbon Capture Utilization and Storage; 2014.
    10. Oladyshkin S. Efficient Modeling of Environmental Systems in the Face of Complexity and Uncertainty [Internet]. Habilitationsschrift Nr. 231, Mitteilungsheft des Instituts für Wasserbau Nr. 231 (Habilitationsschrift) Institut für Wasserbau, Universität Stuttgart, 2014. ISBN: 978-3-942036-35-1; 2014. Available from: http://elib.uni-stuttgart.de/opus/volltexte/2015/9523/

  15. 2013

    1. Oladyshkin S, Class H, Nowak W. Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions:methodology and application to history matching for carbon dioxide storage in geological formations. Computational Geosciences. 2013;17:671–87.
    2. Oladyshkin S, Nowak W. On polynomial chaos expansions under incomplete statistical input information. München, Germany: Euromech colloquium 543; 2013.
    3. Oladyshkin S, Schröder P, Class H, Nowak W. Chaos Expansion based Bootstrap Filter to Calibrate CO2 Injection Models. Energy Procedia. 2013;40:398–407.
    4. Ashraf M, Oladyshkin S, Nowak W. Geological storage of CO2: global sensitivity analysis and risk assessment using arbitrary polynomial chaos expansion. International Journal of Greenhouse Gas Control. 2013;19:704–19.
    5. Flemisch B, Class H, Darcis M, Faigle B, Hommel J, Kissinger A, et al. Coupling Approaches, Risk Assessment and History Matching for CO$_2$ Storage Modeling. Granada, Spain: Plenary lecture; 2013.

  16. 2012

    1. Oladyshkin S, Nowak W. Analyzing the expansion order for polynomial chaos expansions in the light of imprecise information on statistical input distributions. Boulder, CO, USA: 1st International Conference on Frontiers in Computational Physics: Modeling the Earth System; 2012.
    2. Oladyshkin S, Panfilov M. Open thermodynamic model for compressible multicomponent two-phase flow in porous media. Journal of Petroleum Science and Engineering. 2012;81:41–8.
    3. Walter L, Binning PJ, Oladyshkin S, Flemisch B, Class H. Brine migration resulting from CO$_2$ injection into saline aquifers - An approach to risk estimation including various levels of uncertainty. International Journal of Greenhouse Gas Control. 2012;9:495–506.
    4. Walter L, Binning PJ, Oladyshkin S, Flemisch B, Class H. Modeling concepts to address risk of brine infiltration into shallow groundwater resources. Urbana-Champaign, IL, USA: XIX. International Conference on Computational Methods in Water Resources (CMWR); 2012.
    5. Oladyshkin S, Nowak W. Polynomial Response Surfaces for Probabilistic Risk Assessment and Risk Control via Robust Design (Book). Luo Y, editor. Novel Approaches and Their Applications in Risk Assessment, ISBN: 978-953-51-0519-0 [Internet]. 2012; Available from: /brokenurl#www.intechopen.com/books/
    6. Ashraf M, Oladyshkin S, Nowak W. Geological storage of CO$_2$: sensitivity analysis and risk assessment using arbitrary polynomial chaos expansion. In: General Assembly 2012, Geophysical Research Abstracts 14: EGU2012-9243, 2012. Vienna, Austria: European Geosciences Union (EGU); 2012.
    7. Oladyshkin S, Nowak W. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering and System Safety. 2012;106:179–90.
    8. Oladyshkin S, de Barros FPJ, Nowak W. Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology. Advances in Water Resources. 2012;37:10–22.
    9. Walter L, Kissinger A, Oladyshkin S, Helmig R. Methods for evaluating competitive use of the subsurface - for example, the influence of CCS in groundwater. TU, München, Germany: Keynote lecture, Exploratory Workshop: An integrated approach to water research and technology development; 2012.

  17. 2011

    1. Oladyshkin S, Class H, Helmig R, Nowak W. Efficient Bayesian updating with PCE-based particle filters based on polynomial chaos expansion and CO$_2$ storage. In: Fall Meeting 2011, Abstract: GC51A-0928. San Francisco, CA, USA: American Geophysical Union (AGU); 2011.
    2. Enzenhöfer R, Geiges A, Koch J, Leube P, Mehne J, Oladyshkin S, et al. Zurück in die Unsicherheit - Stochastische Modellierung von Hydrosystemen. Stuttgart, Germany: LH2-Kolloquium; 2011.
    3. de Barros FPJ, Oladyshkin S, Nowak W. An integrative data-adaptive approach for global sensitivity analysis: application to subsurface flow and transport. In: General Assembly 2011, Geophysical Research Abstracts 13: EGU2011-11206, 2011. Vienna, Austria: European Geosciences Union (EGU); 2011.
    4. Oladyshkin S, Class H, Helmig R, Nowak W. Bayesian updating on arbitrary polynomial chaos expansion: application to carbon dioxide storage in geological formations. Poitiers, France: Invited lecture: Pprime CNRS, CEAT; 2011.
    5. Oladyshkin S, Class H, Helmig R, Nowak W. A concept for data-driven probabilistic risk assessment and application to carbon dioxide storage in geological formations. Advances in Water Resources. 2011;34:1508–18.
    6. Oladyshkin S, Class H, Helmig R, Nowak W. An Integrative Approach to Robust Design and Probabilistic Risk Assessment for CO$_2$ Storage in Geological Formations. Computer Geosciences. 2011;15:565–77.
    7. Oladyshkin S, Panfilov M. Hydrogen penetration in water through porous medium: application to a radioactive waste storage site. Environmental Earth Sciences. 2011;64:989–99.
    8. Walter A, Binning PJ, Class H, Flemisch B, Oladyshkin S. Brine migration due to CO2 injection into saline aquifers? A consistent approach to risk estimation including different levels of uncertainty. Bergen, Norway: Workshop: IGeMS; 2011.
    9. Walter L, Class H, Oladyshkin S, Flemisch B, Helmig R. Influence of Dirichlet boundary conditions on risk assessment for CO$_2$ storage in geological formations. Urbana-Champaign, IL, USA: SimTech 2011 - International Conference on Simulation Technology 2011; 2011.
    10. Oladyshkin S, Class H, Helmig R, Nowak W. Data-driven framework for history matching: application to carbon dioxide storage in geological formations. In: General Assembly 2011, Geophysical Research Abstracts 13: EGU2011-11137, 2011. Vienna, Austria: European Geosciences Union (EGU); 2011.
    11. Oladyshkin S, Class H, Helmig R, Nowak W. Modeling of underground carbon dioxide storage: data-driven robust design and probabilistic risk assessment. Stuttgart, Germany: SimTech 2011 - International Conference on Simulation Technology 2011; 2011.
    12. Walter L, Oladyshkin S, Class H, Darcis M, Helmig R. A study on pressure evolution in a channel system during CO$_2$ injection. Energy Procedia. 2011;4:3722–9.

  18. 2010

    1. Oladyshkin S, Class H, Helmig R, Nowak W. Probabilistic risk assessment for $CO_2$ storage in geological formations: robust design and support for decision making under uncertainty. In: General Assembly 2010, Geophysical Research Abstracts 12: EGU2010-3559, 2010. Vienna, Austria: European Geosciences Union (EGU); 2010.
    2. Oladyshkin S, Class H, Helmig R, Nowak W. Data-driven framework for modeling of CO$_2$ storage: probabilistic risk assessment, robust design and history matching. Princeton, NJ, USA: Workshop: Scale of resolution, model complexity and solution approaches for CO2 storage problems; 2010.
    3. Walter L, Oladyshkin S, Class H, Darcis M, Helmig R. A study on pressure evolution in a sand channel system during CO$_2$ injection. Amsterdam, The Netherlands: Greenhouse Gas Control Technologies (GHGT10); 2010.
    4. Oladyshkin S, Class H, Helmig R, Nowak W. Chaos expansion for uncertainty quantification of multiphase flow in CO$_2$ storage reservoirs. Nancy, France: JEMP 2010; 2010.
    5. Oladyshkin S, Class H, Helmig R, Nowak W, de Barros FPJ, Ashraf M. Data-driven polynomial response surfaces as efficient tool for applied tasks under uncertainty. Research Triangle Park, NC, USA: SAMSI Geosciences Applications Opening Workshop; 2010.
    6. Oladyshkin S, Class H, Hofmann R, Nowak W. Highly efficient tool for probabilistic risk assessment of CCS joint with injection design. Barcelone, Spain: XVIII. International Conference on Computational Methods in Water Resources (CMWR); 2010.
    7. Oladyshkin S, Class H, Helmig R, Nowak W. Data-driven robust design and probabilistic risk assessment: application to underground carbon dioxide storage. In: Fall Meeting 2010, Abstract: H41L-03. San Francisco, CA, USA: American Geophysical Union (AGU); 2010.

  19. 2009

    1. Oladyshkin S, Nowak W, Helmig R. Joint design and probabilistic risk assessment for CO$_2$ storage by integral probabilistic collocation method. Stuttgart, Germany: International conference on Non-linearities and Upscaling in Porous Media (NUPUS); 2009.

  20. 2008

    1. Oladyshkin S, Royer JJ, Panfilov M. Effective solution through the streamline technique and HT-splitting for the $3$D dynamic analysis of the compositional flows in oil reservoirs. Transport in Porous Media. 2008;74:311–29.
    2. Oladyshkin S. HT-splitting and open thermodynamic model for compressible multicomponent two-phase flow in porous media. Nancy, France: EUROMECH Colloquium 499, Nonlinear Mechanics of Multiphase Flow in Porous Media: Phase Transitions, Instability, Non equilibrium, Modelling; 2008.
    3. Oladyshkin S, Panfilov M. Differential split thermodyamic model for gasliquid compositional flow. Lyon, France: MoMaS Journ’ees Multiphasiques; 2008.

  21. 2007

    1. S. Oladyshkin J-JR, Panfilov M. Modelling Compositional Flows in Oil Reservoirs using gOcad Streamline Simulator and HT-Splitting Technique. In: 27th Gocad Meeting. Nancy, France; 2007.
    2. Oladyshkin S, Panfilov M. Limit thermodynamic model for compositional gas-liquid systems moving in a porous medium. Transport in Porous Media. 2007;70:147–65.
    3. Oladyshkin S, Panfilov M. Hydro-thermodynamics of multi-compositional flow in porous media: modelling of hydrogen-water flow with mass exchange in an underground storage. St-Raphael, France: Mini-cours. GdR MoMaS, Modélisation Numérique d’Écoulements Multiphasiques en Milieux Poreux : Application au Transfert des Gaz autour du Stockage de Déchets Radioactifs; 2007.
    4. Oladyshkin S, Skachkov S, Panfilova I, Panfilov M. Upscaling fractured media and streamline HT-splitting in compositional reservoir simulation. Oil & Gas Science and Technology. 2007;62:137–46.
    5. Oladyshkin S, Panfilov M. Streamline splitting between thermodynamics and hydrodynamics in compositional gas-liquid flow through porous media. Comptes rendus de l’Academie des sciences Mecanique. 2007;335:7–12.

  22. 2006

    1. Oladyshkin S, Panfilov M. Gas-liquid flow in porous media, HT-splitting and asymptotic solutions. Nancy, France: The 4th Seminar “Sciences and Engineering of Resources, Techniques, Produced, Environment”; 2006.
    2. Oladyshkin S, Panfilova I, Panfilov M. Non-Equlibrium Two-Velocity Effects in Gas-Condensate Flow through Porous Media. In: ECMOR-X :10th European Conference on the Mathematics of Oil Recovery P. B029. Amsterdam, Netherlands; 2006. p. 10.
    3. Oladyshkin S, M. Panfilov IP, Skachkov S. Streamline splitting the thermo- and hydrodynamics in compositional gas-liquid flow through porous media and application to hydrogen - water behaviour in radioactive waste deposits. Pau, France: GdR MoMaS, Modélisation Numérique d’Écoulements Multiphasiques en Milieux Poreux : Application au Transfert des Gaz autour du Stockage de Déchets Radioactifs; 2006.
    4. Oladyshkin S, Panfilov M. Splitting the Thermodynamics and Hydrodynamics in Compositional Gas-Liquid Flow through Porous Reservoirs. In: ECMOR-X :10th European Conference on the Mathematics of Oil Recovery. P. B030. Amsterdam, Netherlands; 2006. p. 10.
    5. S. Oladyshkin, M. Panfilov IP, Shandrygin A. Two-phase flow of a retrograde mixture in the porous media: non-equlibrium effects and splitting the thermodynamlics and hydrodynamics. In: IX Russian Congress of Theoretical and Applied Mechanics. Nizhniy Novgorod, Russia; 2006. p. 140.
    6. Oladyshkin S, M. Panfilov IP, Skachkov S. Upscaling the macro-fractures in compositional reservoir simulation: streamline HT-splitting and analytical boundary method. Rueil-Malmaison, France: Int. Conf. Quantitative Methods for Reservoir Characterization. IFP,; 2006.

  23. 2005

    1. Oladyshkin S, Panfilov M. Asymptotic semi-stationary contrast model of gas-liquid flow with phase transitions in porous media. Avignon, France: Int. Conference SIAM : Society for Industrial and Applied Mathematics, “Mathematical and Computational Issues in the Geosciences”; 2005. p. 35.
    2. Oladyshkin S, Panfilov M. Asymptotic analytical model of gas-condensate flow in porous media. In: 3rd Seminar “Sciences and Engineering of Resources, Techniques, Production, Environment”. Nancy, France; 2005. pp. 276–83.
    3. S. Oladyshkin MP. Modeling of two-phase macroflow with phase transitions and contract properties. Transactions of the Russian Academy of Engineering Sciences. 2005;5:34–6.
    4. Oladyshkin S, Panfilov M. Two-phase flow with phase transitions in porous media: instability of stationary solutions and a semi-stationary model. In: Third Biot Conference on Poromechanics. Norman, OK, USA; 2005. pp. 529–35.

  24. 2003

    1. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Invariant immersing method applied to the problem to thermocapillary convection a viscous fluid in the plane channel. Transactions of the Russian Academy of Engineering Sciences. Ser. Applied Mathematics and Mechanics. 2003;4:26–31.
    2. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Influence of the Marangoni thermocapillary effect on thermal distribution in the modelling environment with space conditions. Ekaterinbourg, Russia: UrO RAN; 2003. p. 52.
    3. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Influence of Marangoni forces on distribution of temperature in the viscous fluid filling a two-sided corner. Moscow-Izhevsk, Russia: The 3 international conference “Mathematics. Computer. Education”; 2003. p. 90.

  25. 2002

    1. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Temperature crisis in the viscous fluid of finite thermoconductivity, which motion under the action of Marangoni forces. Sarov, Russia: The VII N. Novgorod session of young scientists (section of mathematics and mathematical modelling); 2002. pp. 58–9.
    2. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Algorithm of invariant immersing as applied to thermocapillary convection a viscous liquid in a plane channel. N. Novgorod, Russia: The VII computer-based conference «Information technologies in science, designing and manufacture»; 2002. p. 9.
    3. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. The simulation of viscous fluid convection in the plane channel under the thermocapillary forces action. Moscow-Dubna, Russia: The 3 international conference “Mathematics. Computer. Education”; 2002. p. 121.
    4. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Modeling of temperature and velocity field inside viscous fluid of finite thermoconductivity, moving inside a corner with free surface, under the action of Marangoni forces. Transactions of the Russian Academy of Engineering Sciences. Ser. Applied Mathematics and Mechanics. 2002;79–88.

  26. 2001

    1. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. The simulation of viscous fluid convection in the plane channel under the thermocapillary forces action. N. Novgorod, Russia: The 3 international scientific-practical conference on graphic information technologies and systems (COGRAPH); 2001. p. 13.
    2. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. Marangoni effect, when the surface tension coefficient depends non-linearly on the temperature. Ekaterinburg, Russia: 3 All-Russian congress on theoretical and applied mechanics. Ural department of Russian Academy of Sciences; 2001. p. 15.
    3. S. Oladyshkin, N.N. Bobkov YuPG, Kozyrev OR. The numerical algorithm development in the problem of thermocapillary convection under the Marangoni forces action. Transactions of the Russian Academy of Engineering Sciences. 2001;2:28–39.

07/2002 Angewandte Mathematik (Dipl.), Lobachevsky Staatliche Universität zu Nizhny Novgorod, Russland
10/2003 - 12/2008 Wissenschaftlicher Mitarbeiter, Universität Lorraine, Nancy, Frankreich
10/2006 Promotion, Universität Lorraine, Nancy, Frankreich
02/2009-02/2014  Wissenschaftlicher Mitarbeiter, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart
02/2014 Habilitation, Universität Stuttgart 
Since 09/2014 stellvertretender Leiter der Abteilung Stochastische Simulation und Sicherheitsforschung für Hydrosysteme, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart
Seit 09/2020 apl. Professor, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart
Seit 10/2021 Studiendekan “Water Resources Engineering and Management”, Universität Stuttgart

Umwelttechnik: Kohlendioxidspeicherung, unterirdische Lagerstätten, Lagerung radioaktiver Abfälle
Unterirdische Strömung: Strömung in porösen Medien, mehrphasige Strömung, Strömung in der Zusammensetzung
Quantifizierung von Unsicherheiten und maschinelles Lernen: Polynomiale Chaos-Erweiterung, Gauß-Prozess-Emulator, Sensitivitätsanalyse, Risikobewertung, Modellkalibrierung, Modellauswahl, Bayes'sche Inferenz, Informationstheorie

Projekte:

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