Conferences

Department of Stochastic Simulation and Safety Research for Hydrosystems

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  1. 2025

    1. 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.
    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. Pawusch L, Scheurer S, Nowak W, Maxwell R. Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2025; 2025.
    5. 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.
    6. Pawusch L, Scheurer S, Nowak W, Maxwell R. Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2025; 2025.
    7. 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.
    8. 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.
    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. Seidel J, Einfalt T, Jessen M, A. B, El Hachem A, Treis A. Using personal weather station data for improving precipitation estimates and gauge adjustment of radar data. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.
    5. 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.
    6. 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.
    7. Scheurer S, Nowak W. Neural Process Regression. In: geoENV2024 Book of Abstracts. Chania, Crete, GR: Creative Commons Licence BY-NC-ND 4.0; 2024. pp. 155–6.
    8. 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.
    9. Ejaz F, Wildt N, Wöhling T, Nowak W. Estimating catchment-wide total groundwater storage via space-time kriging provides calibration data for catchment-scale groundwater balance models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.
    10. Martin P, Haas J, Nowak W. Coordinated Optimization of Water-Energy System Operation. In: EURO 2024 Conference Handbook and Abstracts. Copenhagen DK: 33rd European Conference on Operational Research (EURO 2024); 2024.
    11. Weber TK, Schade A, Rauch R, Ingwersen J, Gayler S, Nowak W, et al. Bayesian Methods for Parameter Inference, Uncertainty Quantification, Error Modelling, Model Learning and Model Choice in Hydrology. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2024; 2024.
    12. Amin B, Bàrdossy A, Haberlandt U. Uncertainty Quantification of Theoretical Consistent Intensity Duration Frequency (IDF) Curves of Rainfall Intensity. In: Geophys. Res. Abstr. Virtual: EGU General Assembly 2024; 2024.
    13. Bruennette T, Nowak W. Efficient Inference for Non-Deterministic Fractures. In: geoENV2024 Book of Abstracts. Chania, Crete, GR: Creative Commons Licence BY-NC-ND 4.0; 2024. pp. 67–8.

  3. 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. Martin P, Haas J, Nowak W. Coordinated Optimization of Water Supply Systems with Evolving Energy Systems. In: Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU); 2023.
    3. Bruennette T, Werneck L, Keip M-A, Nowak W. Random Fracture Models - Towards Statistical Realism and Validation. In: Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU); 2023.
    4. Wildt N, Scheurer S, Nowak W, Haslauer C. Learning PFAS mechanisms with a FInite Volume Neural Network (FINN). In: Fall Meeting 2023. San Francisco, CA, USA: American Geophysical Union (AGU); 2023.
    5. Amin B, Bàrdossy A. Comparative Analysis of Parameter Optimization of Theoretically Consistent IDF Models of Rainfall Intensity. In: Geophys. Res. Abstr. Virtual: EGU General Assembly 2023; 2023.

  4. 2022

    1. Takamoto M, Praditia T, Leiteritz R, MacKinlay D, Alesiani F, Pflüger D, et al. PDEBench: An Extensive Benchmark for Scientific Machine Learning. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. 2022.
    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. 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).

  5. 2021

    1. 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
    2. Praditia T, Oladyshkin S, Nowak W. Physics Informed Neural Network for porous media modelling. Stuttgart, Germany: InterPore German Chapter Meeting 2021; 2021.
    3. Praditia T, Oladyshkin S, Nowak W. Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow. online: EGU General Assembly 2021; 2021.
    4. Wei R. Modeling combined with fieldwork on surface water quality: the sources and fate of mixture effects under unsteady flow. In: Integrated Hydrosystem Modeling Conference. Tübingen, Germany: Integrated Hydrosystem Modeling Group; 2021.
    5. Wei R. Modeling combined with fieldwork on surface water quality: the sources and fate of mixture effects under unsteady flow. In: Fall Meeting 2021. New Orleans, LA, USA & online: American Geophysical Union (AGU); 2021.
    6. 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.

  6. 2020

    1. Nowak W, Xu T, Reuschen S, Hendricks Franssen H-J, Guadagnini A. High-end solution techniques and accurate reference solutions: towards a community-wide benchmarking effort for stochastic inverse modeling of groundwater flow. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    2. Moreno Leiva S, Haas J, Nowak W, Junne T. Flexible energy systems for planning the world’s main copper mines considering geographical conditions. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    3. Gonzalez-Nicolas Alvarez A, Nowak W, Sinsbeck M, Schwientek M. Characterize the catchment regime by applying optimal monitoring strategies. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    4. Schäfer Rodrigues Silva A, Höge M, Guthke A, Nowak W. Comparing different approaches for modeling reactive transport on aquifer-scale – which level of complexity is legitimate? In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    5. Moreno Leiva S, Haas J, Nowak W, Junne T. Flexible energy systems for planning the world’s main copper mines considering geographical conditions. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    6. Reuschen S, Xu T, Nowak W. Bayesian inversion and visualization of hierarchical geostatistical models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    7. Erdal D, Xiao S, Nowak W, Cirpka OA. Effective sampling of behavioral subsurface parameter realizations assisted by surrogate models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    8. Haas J, Mahmoud M, Yeligeti M, Moreno-Leiva S, Nowak W, Palma-Behnke R. Adaptive Planning: Finding Optimal Energy Pathways In Times Of Deep Uncertainty. Düsseldorf, Germany: International Renewable Energy Storage (IRES) Conference; 2020.
    9. Gonzalez-Nicolas Alvarez A, Nowak W, Sinsbeck M, Schwientek M. Characterize the catchment regime by applying optimal monitoring strategies. In: EGU General Assembly Conference Abstracts. 2020. p. 4843. (EGU General Assembly Conference Abstracts).
    10. Ejaz F, Wöhling T, Nowak W. Lumped hydrological model for reasonable, long- term predictions of groundwater storage and depletion. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    11. Ejaz F, Wöhling T, Nowak W. Lumped hydrological model for reasonable, long- term predictions of groundwater storage and depletion. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    12. Reuschen S, Xu T, Nowak W. Bayesian inversion and visualization of hierarchical geostatistical models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    13. Gonzalez-Nicolas A, Allgeier J, Erdal D, Nowak W, Cirpka OA. Optimal monitoring strategies for minimal uncertainties about a groundwater divide. In: SIAM Conference on Uncertainty Quantification (UQ20) [Internet]. 2020. (SIAM UQ). Available from: https://www.events.tum.de/frontend/index.php?page_id=3712&v=List&do=15&day=all&ses=1712#
    14. Allgeier J, Gonzalez-Nicolas A, Erdal D, Nowak W, Cirpka OA. A Stochastic Framework to Optimize the Monitoring Strategy for the Delineation of a Groundwater Divide. In: EGU General Assembly Conference Abstracts. 2020. p. 6907. (EGU General Assembly Conference Abstracts).
    15. Gonzalez-Nicolas Alvarez A, Nowak W, Sinsbeck M, Schwientek M. Characterize the catchment regime by applying optimal monitoring strategies. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    16. Schäfer Rodrigues Silva A, Höge M, Guthke A, Nowak W. Comparing different approaches for modeling reactive transport on aquifer-scale – which level of complexity is legitimate? In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    17. Allgeier J, Gonzalez-Nicolas A, Erdal D, Nowak W, Cirpka OA. A Stochastic Framework to Optimize the Monitoring Strategy for the Delineation of a Groundwater Divide. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    18. Erdal D, Xiao S, Nowak W, Cirpka OA. Effective sampling of behavioral subsurface parameter realizations assisted by surrogate models. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    19. Nowak W, Xu T, Reuschen S, Hendricks Franssen H-J, Guadagnini A. High-end solution techniques and accurate reference solutions: towards a community-wide benchmarking effort for stochastic inverse modeling of groundwater flow. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.
    20. Allgeier J, Gonzalez-Nicolas A, Erdal D, Nowak W, Cirpka OA. A Stochastic Framework to Optimize the Monitoring Strategy for the Delineation of a Groundwater Divide. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2020; 2020.

  7. 2019

    1. Junne T, Haas J, Wang J, Moreno S, Naegler T, Buchgeister J, et al. Considering Ecological Sustainability in Planning the Future Electricity Supply of Chile – How Much More Does it Cost? Düsseldorf, Germany: International Renewable Energy Storage (IRES) Conference; 2019.
    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. Moreno-Leiva S, Haas J, Junne T, Kracht W, Eltrop L. Strategic scheduling of maintenance in copper processing as a flexibility option for highly renewable energy systems: a sample of a solar-paced design. In: Annual Conference of Metallurgists. Vancouver, BC, Canada; 2019.
    4. Pamparana G, Kracht W, Ortiz J, Haas J. Understanding the effect of ore hardness variability on the integration of solar energy into the operation of a semi-autogenous grinding mill. In: Conference of Metallurgist / Copper. Vancouver, BC, Canada: Conference of Metallurgist / Copper; 2019.
    5. Maier R, Gonzalez-Nicolas A, Leven C, Nowak W, Cirpka OA. Choosing Between Heterogeneity and Anisotropy - What’s in the Data and What Do Your Purposes Require? In: AGU Fall Meeting Abstracts. 2019. pp. H21L–1910.
    6. González-Nicolás A, Nowak W, Sinsbeck M, Schwientek M. Optimal scheduling and event-based sampling: development and demonstration for investigating the nitrate characteristics of a headwater catchment. In: General Assembly 2019, Geophysical Research Abstracts: EGU2019-964, 2019. Vienna, Austria: European Geosciences Union (EGU); 2019.
    7. Allgeier J, González-Nicolás A, Cirpka O, Nowak W, Finchel M. Where to draw the line? In: Groundwater quality in the transition between rural and urban environments Conference, Poster. Liege, Belgium; 2019.
    8. Vera-Hofmann G, Haas J, Moreno-Leiva S, Díaz F, Eltrop L. How to plan a full-renewable multi-vector energy system to be resilient? Vol. Solar World Congress. Santiago, Chile; 2019.
    9. Gonzalez P, Haas J, Rahmann C, Alvarez R, Retanz C. Generation expansion planning considering storage systems and inertial constraints. Vol. Solar World Congress. Santiago, Chile; 2019.

  8. 2018

    1. Haas J, Nowak W. What technical phenomena are relevant when designing the optimal energy storage mix? Stuttgart, Germany: 2nd International Conference on Simulation Technology; 2018.
    2. Guthke A. A Bayesian take on model choice uncertainty: Statistical tools for model evaluation, selection and combination. Karlsruhe, Germany: KIT, Institut für Wasser und Gewässerentwicklung, Lehrstuhl für Hydrologie; 2018.
    3. González-Nicolás A, Nowak W. Optimal sampling strategies for river water quality to determine the chemostatic strength of catchments (GRS, Oral Presentation). In: GRS: Flow and Transport in Permeable Media, Understanding Complexity for Prediction in Permeable Media, Oral. Newry, ME, USA: Gordon Research Conferences (GRC); 2018.
    4. Schäfer-Rodrigues-Silva A, Seitz T, Guthke Anneli, Nowak W. Working with multi-model ensembles - what makes models differ and how can we visualize ensembles? Tübingen, Germany: Seminar of the Research Training Group “Integrated Hydrosystem Modelling”; 2018.
    5. González-Nicolás A, Nowak W. Optimal sampling strategies for river water quality to determine the chemostatic strength of catchments (Integrated Hydrosystem Modelling, Poster Presentation). In: Integrated Hydrosystem Modelling, Poster. Tübingen, Germany: Research Training Group “Integrated Hydrosystem Modelling”; 2018.
    6. Ejaz F, Fatkhutdinov A, Stefan C, Usman M. Integration of raster based irrigation and groundwater for water management in Punjab, Pakistan: A modeling & GIS based approach. Riyadh, Saudi Arabia: 8th International Conference on Water Resources and Arid Environments; 2018.
    7. Thoms A, Chow R, Steelman C, Nowak W, Parker B. Simulating Hyporheic Exchange in Bedrock Rivers - The Eramosa Bedrock River Field Site. In: Fall Meeting 2018. Washington, D.C., USA: American Geophysical Union (AGU); 2018.
    8. Haas J, Nowak W, Cebulla F, Rahmann C, Palma-Behnke R. A multi-service approach for finding the optimal energy storage mix for renewable systems. Düsseldorf, Germany: International Renewable Energy Storage (IRES) Conference; 2018.
    9. Schäfer-Rodrigues-Silva A, Nowak W. What is a legitimate model complexity? Tübingen, Germany: Meeting of the CRC 1253 CAMPOS - Catchments as Reactors: Metabolism of Pollutants on the Landscape Scale; 2018.
    10. González-Nicolás A, Nowak W. Optimal sampling strategies for river water quality to determine the chemostatic strength of catchments (GRC, Poster Presentation). In: GRC: Flow and Transport in Permeable Media, From Pore-Scale Physics to Geologic-Scale Processes, Poster. Newry, ME, USA: Gordon Research Conferences (GRC); 2018.
    11. Schäfer-Rodrigues-Silva A, Guthke A, Nowak W. The importance of model similarity in multi-model problems. Stuttgart, Germany: Meeting of the international doctoral program “Environment Water”; 2018.
    12. Ortiz J, Kracht W, Pamparana G, Haas J. Integrating uncertainty in rock hardness and solar irradiation in the optimization of a SAG mill energy system. In: 12th International Conference on Geostatistics for Environmental Applications (geoENV). Belfast, North Ireland; 2018.
    13. Moreno S, Haas J, Junne T, Eltrop L. A green copper concept: on the design of a full-renewable energy supply for its production. In: International Symposium on Energy System Optimization. Karlsruhe, Germany: ISESO; 2018.
    14. Schäfer-Rodrigues-Silva A, Guthke Anneli, Nowak W. Quantifying similarity in multi-model ensembles. Tübingen, Germany: International Conference on Integrated Hydrosystem Modelling; 2018.
    15. Schäfer-Rodrigues-Silva A, Seitz T, Guthke Anneli, Nowak W. Quantifying and visualizing similarity in multi-model ensembles. Cargese, France: Summer school on “Flow and Transport in Porous and Fractured media”; 2018.
    16. González-Nicolás A, Nowak W, Shäfer A. Optimization of strategies to collect data of the field. Tübingen, Germany: 1st Science Meeting within CAMPOS project; 2018.
    17. Rodriguez-Pretelin A, Nowak W. Reducing the influence of transient flow for improved Wellhead Protection: An engineered optimal pumping-injection management solution. Stuttgart, Germany: 2nd International Conference on Simulation Technology; 2018.
    18. Höge M, Wöhling T, Nowak W. Model Selection: Play-It-Safe vs. No-Risk-No-Fun. In: Integrated Hydrosystem Modelling 2018 Conference: How Complex Should Integrated Models Be? Tübingen, Germany: RTG 1829, DFG; 2018.

  9. 2017

    1. Sinsbeck M, Nowak W. Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. Weimar, Germany: 88th GAMM Annual Meeting; 2017.
    2. Haas J, Nowak W. Water-energy nexus: challenges from Chile. Golden, Colorado: International Conference on Energy Systems Integration (ICESI); 2017.
    3. Schäfer-Rodrigues-Silva A, Nowak W. Model legitimacy - Model complexity in the light of limited data. Stuttgart, Germany: Meeting of the international doctoral program “Environment Water”; 2017.
    4. Haas J, Nowak W, Palma-Behnke R. Multiple Storage Technologies Offering Multiple Services in Generation Expansion Planning. Düsseldorf, Germany: International Renewable Energy Storage (IRES) Conference; 2017.
    5. Praditia T, Helmig R, Hajibeygi H. Multiscale finite volume method for sequentially coupled flow-heat system of equations in fractured porous media: application to geothermal systems. Erlangen, Germany: SIAM Conference on Mathematical and Computational Issues in the Geosciences; 2017.

  10. 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. Sinsbeck M, Nowak W. Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. Lausanne, Switzerland: SIAM Conference on Uncertainty Quantification; 2016.
    3. Most S, Nowak W, Bijeljic B. Let’s move to spheres! Why a spherical coordinate system is rewarding when analyzing particle increment statistics. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2016; 2016.
    4. Moreno-Leiva S, Román R, Kracht W, Palma-Behnke R, Haas J, Díaz-Ferrán G. Integrating solar energy in copper mining industry: preliminary analysis based on LCA. In: International Seminar on Energy Management in Mining. Santiago, Chile; 2016.
    5. Moreno-Leiva S, Telsnig T, Haas J, Díaz-Alvarado F, Díaz-Ferrán G, Palma-Behnke R, et al. Solar Mining: integraci’on de tecnologías solares en la minería desde la perspectiva ACV (Solar Mining: integration of solar technologies into mining from a LCA perspective). In: I Conferencia Chilena de ACV. Viña del Mar, Chile: Red Ibero-Americana de Ciclos de Vida (RICV); 2016.
    6. Schöniger A, Illman W, Wöhling T, Nowa W. Which level of model complexity is justified by your data? A Bayesian answer. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2016; 2016.
    7. Trevisan L, González-Nicolás A, Cihan A, Pini R, Birkholzer J, Illangasekare T. Experimental and modeling study of capillary/buoyancy-driven flow of surrogate CO2 through intermediate-scale sand tanks, Greenhouse Gas Control Technologies. Lausanne, Switzerland: Greenhouse Gas Control Technologies, GHGT-13; 2016.
    8. Most S, Bijeljic B, Nowak W. A copula-based analysis of the dependence structure and process memory of anomalous pore-scale transport. Cincinnati OH, USA: Interpore International Society for Porous Media: 8th International Conference on Porous Media & Annual Meeting; 2016.
    9. Pamparana G, Haas J, Díaz-Ferrán G, Kracht W, Palma-Behnke R, Román R. Operational planning of Semi-Autogenous Grinding (SAG) copper mills, photovoltaic and battery energy storage system. In: 3rd International Seminar on Energy Management in Mining. Santiago, Chile; 2016.
    10. Schöniger A, Illman W, Wöhling T, Nowa W. Which level of model complexity is justified by your data? A Bayesian answer. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2016; 2016.
    11. Most S, Nowak W, Bijeljic B. Let’s move to spheres! Why a spherical coordinate system is rewarding when analyzing particle increment statistics. In: Geophys. Res. Abstr. Vienna: EGU General Assembly 2016; 2016.
    12. 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. 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.

  11. 2015

    1. González-Nicolás A, Illangasekare T. Enhancing Trapping Effectiveness through the Optimization of Injection of Supercritical CO2 in Heterogeneous Formations. Padova, Italy: 7th International Conference on Porous Media; 2015.
    2. Schöniger A. Multi-model approaches to quantify conceptual uncertainty in environmental modelling. Tübingen, Germany: International Conference on Integrated Hydrosystem Modelling; 2015.
    3. Most S, Bijeljic B, Nowak W. A complete fingerprint of non-Fickian mixing – an empirical statistical analysis. Padova, Italy: Interpore International Society for Porous Media: 7th International Conference on Porous Media & Annual Meeting; 2015.
    4. Haas J, Nowak W, Palma-Behnke R, Cebulla F. Energy storage opportunities and modelling challenges in Chile. Concepcion, Chile: Production and Storage of Renewable Energy, KIT Alumni Expert Seminar; 2015.
    5. 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.
    6. Lötgering-Lin O, Hopp M, Gross J, Schöniger A, Nowak W. Prediction of pure component and mixture viscosities using PCP-SAFT and entropy scaling. Houston, TX, USA: SAFT 2015; 2015.

  12. 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. 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.
    3. 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.
    4. Bode F, Binning P, Nowak W. Multi-Objective Optimization of Groundwater Monitoring Networks: Computational Costs vs. Optimization Accuracy. Stuttgart, Germany: XX. International Conference on Computational Methods in Water Resources (CMWR); 2014.
    5. Geiges A, Nowak W. A reverse engineering approach to optimize the design of experiments and field campaigns. Stuttgart, Germany: XX. International Conference on Computational Methods in Water Resources (CMWR); 2014.
    6. Bode F, Binning PJ, Nowak W. Enhanced Multi-objective Optimization of Groundwater Monitoring Networks. Denver, CO, USA: NGWA Groundwater Summit 2014; 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. Sinsbeck M, Nowak W. Adaptive Sampling for Bayesian Updating with Non-Intrusive Polynomial Chaos Expansions. Savannah, Georgia, USA: SIAM Conference on Uncertainty Quantification; 2014.

  13. 2013

    1. Cody B, González-Nicolás A, Baú D. Analyzing Potential Improvements to a Semi-Analytical CO2 Leakage Algorithm. Fort Collins, CO, USA: XXXII American Geophysical Union Hydrology Days; 2013.
    2. Oladyshkin S, Nowak W. On polynomial chaos expansions under incomplete statistical input information. München, Germany: Euromech colloquium 543; 2013.
    3. Tanaka N, Mehne J, Nowak W, Danzer M, Döring H, Bessler WG. Numerical Simulation and Experimental Validation for Thermal Runaway on Lithium-ion Cells. Bad boll, Germany: 10th Symposium for Fuel Cell and Battery Modelling and Experimental Validation; 2013.
    4. González-Nicolás A, Cody B, Baú D. Stochastic Optimization of the Geological Sequestration of Carbon Dioxide. Fort Collins, CO, USA: XXXIII American Geophysical Union Hydrology Days; 2013.
    5. Mehne J, Nowak W. Stochastic Extension of a Thermal-Electrochemical Lithium-Ion Battery Model. San Francisco, CA, USA: 224th ECS meeting; 2013.
    6. González-Nicolás A, Cody B, Baú D. Estimation of the Sealing Properties of MTU-site (Michigan) for Geological Carbon Storage. Fort Collins, CO, USA: XXXIII American Geophysical Union Hydrology Days; 2013.
    7. Enzenhöfer R, Binning PJ, Nowak W. Challenges & Limitations in Risk Assessment and Aggregation (The STORM Framework. Freudenstadt, Germany: NUPUS annual Meeting: : Institut für Wasserbau, Lehrstuhl für Hydromechanik und Hydrosystemmodellierung; 2013.
    8. González-Nicolás A, Cody B, Baú D. Carbon geological sequestration: effects of parameter uncertainty on fluid overpressure and CO2 leakage. Fort Collins, CO, USA: XXXII American Geophysical Union Hydrology Days; 2013.
    9. Bode F, Nowak W. Optimization Early-Warning Monitoring Systems for Improved Drinking Water Resource Protection. NUPUS annual Meeting: Institut für Wasserbau, Lehrstuhl für Hydromechanik und Hydrosystemmodellierung; 2013.
    10. 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.

  14. 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. 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.
    3. Cody B, González-Nicolás A, Baú D. Optimization of Geological Carbon Sequestration using Semi-Analytical Leakage Models linked to a Multi-objective Evolutionary Algorithm. Urbana, IL, USA: XIX. International Conference on Computational Methods in Water Resources (CMWR); 2012.
    4. Mehne J, Nowak W, Danzer M, Döring H, Tanaka N, Bessler WG. Towards Predicting Thermal Runaway of Lithium-Ion Batteries. Ulm, Germany: 13th Ulmer Electrochemical Talks; 2012.
    5. Enzenhöfer R, Bunk T, Nowak W, Enzenhöfer R, Bunk T, Nowak W. Nine Steps to risk-informed well-head protection and management via probabilistic vulnerability criteria. Paris, France: Groundwater Vulnerability- Emerging Issues and New Approaches (IMVUL); 2012.
    6. Enzenhöfer R, Nowak W. Well-head Protection: Time matters? But how much? Mühlheim an der Ruhr, Germany: NUPUS annual Meeting: Institut für Wasserbau, Lehrstuhl für Hydromechanik und Hydrosystemmodellierung; 2012.
    7. 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.
    8. González-Nicolás A, Cody B, Baú D. Stochastic analysis of factors affecting the leakage of CO2 from injected geological basins. Urbana, IL, USA: XIX. International Conference on Computational Methods in Water Resources (CMWR); 2012.
    9. Enzenhöfer R, Rodriguez-Pretelin A, Nowak W. Probabilistic well-head protection under transient flow conditions. Paris, France: Groundwater Vulnerability- Emerging Issues and New Approaches (IMVUL); 2012.

  15. 2011

    1. 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.
    2. 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.
    3. Enzenhöfer R, Nowak W. Quantitative Aggregierung von Einzelrisiken zum Gesamtrisiko für Brunneneinzugsgebiete. Mannheim, Germany: F&E-Projekttreffen: W 1/01/10 Risikomanagement für Wasserschutzgebiete; 2011.
    4. González-Nicolás A, Cody B, Baú D. Numerical simulation of CO2 injection into deep saline aquifers. Fort Collins, CO, USA: XXXI American Geophysical Union Hydrology Days; 2011.
    5. Nowak W, de Barros FPJ, Rubin Y. A hypothesis-driven approach to optimal site investigation. Stuttgart, Germany: SimTech 2011 - International Conference on Simulation Technology 2011; 2011.
    6. Enzenhöfer R, Nowak W. Optimal designed risk catchment management by means of proabilistic well vulnerability criteria. Mühlheim an der Ruhr, Germany: Strategic Asset Management of Water and Wastewater Infrastructure; 2011.
    7. Chow R, Frind EO, Sousa M, Jones JP, Rudolph DL, Molson JW. Delineating Capture Zones for Environmentally Sensitive Features– A Model Comparison. In: Geohydro Conference. Quebec City, Quebec Canada; 2011.
    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. Enzenhöfer R, Geiges A, Nowak W, Helmig R. Optimal risk management support in actively managed well catchments by means of probabilistic vulnerability criteria. Stuttgart, Germany: SimTech 2011 - International Conference on Simulation Technology 2011; 2011.
    11. de Barros FPJ, Oladyshkin S, Nowak W. An integrative data-adaptive approach for global sensitivity analysis: application to subsurface flow and transport. Stuttgart, Germany: SimTech 2011 - International Conference on Simulation Technology 2011; 2011.
    12. Cody B, González-Nicolás A, Baú D. Multi-site CO2 Sequestration Optimization using a Dynamic Programming Approach. Fort Collins, CO, USA: XXXI American Geophysical Union Hydrology Days; 2011.
    13. 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.
    14. Enzenhöfer R, Nowak W. Risikomanagement im Grundwasserschutzgebiet - ein präventives probabilistisches Trinkwasserschutzkonzept. Stuttgart, Germany: Invited lecture: 25.Trinkwasserkolloquium; 2011.
    15. Enzenhöfer R, Nowak W, Helmig R. Probabilistic exposure risk assessment with advective-dispersive well vulnerability criteria. Freudenstadt, Germany: NUPUS annual Meeting: Institut für Wasserbau, Lehrstuhl für Hydromechanik und Hydrosystemmodellierung; 2011.
    16. Flemisch B, Bode F, Braun J. Thermisch genutzte P&T-Anlagen - Chancen und Risiken. Stuttgart, Germany: VEGAS-Kolloquium 2011; 2011.

  16. 2010

    1. Nowak W, de Barros FPJ, Geiges A, Leube P. Optimal Site Investigation: What data to collect so that calibrated models have best prognistic power? Tübingen, Germany: Invited lecture: FH-DGG Tagung 2010, Grundwasser für die Zukunft; 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. Enzenhöfer R, Nowak W, Helmig R. Quantification of Uncertainty for Transport-QBased Well Vulnerability Criteria. Freudenstadt, Germany: NUPUS annual Meeting: Institut für Wasserbau, Lehrstuhl für Hydromechanik und Hydrosystemmodellierung; 2010.
    5. 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.
    6. Nowak W, de Barros FPJ, Rubin Y. Hypothesis-driven site investigation. Monte Veritá, Switzerland: Thirty years of stochastic subsurface hydrology: Where do we stand and what are the emerging challenges; 2010.
    7. 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.
    8. 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.
    9. Nowak W. Challenges and demands from the application side: An example from CO$_2$ injection into the subsurface. Stuttgart, Germany: Workshop: Numerical Analysis of Stochastic PDEs (NASPDE); 2010.

  17. 2009

    1. Nowak W. Stochastic Simulation and Bayesian Principles, with Examples from Stochastic Hydrogeology. Stuttgart, Germany: ASIM-Workshop: Grundlagen und Methoden der Modellbildung und Simulation; 2009.
    2. 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.
    3. Nowak W. Uncertainty analysis in hydrometry: parameter and prediction uncertainty in subsequent modelling. Vancouver, Canada: 33rd IAHR Congress: Water Engineering for a Sustainable Environment; 2009.

  18. 2008

    1. 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.
    2. Fienen M, Hunt RJ, Clemo T, Nowak W, Doherty J. Implementation of a bayesian geostatistical parameter estimation module into PEST. Orange Beach, AL, USA: USGS Modeling Conference; 2008.
    3. Oladyshkin S, Panfilov M. Differential split thermodyamic model for gasliquid compositional flow. Lyon, France: MoMaS Journ’ees Multiphasiques; 2008.

  19. 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. 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.

  20. 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.

  21. 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.

  22. 2003

    1. 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.
    2. 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.

  23. 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.

  24. 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.
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