Journals

Stochastische Simulation und Sicherheitsforschung für Hydrosysteme

  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 (Submitted)

    1. Ejaz F, Wöhling T, Guthke A, Nowak W. Comprehensive uncertainty analysis for surface water and groundwater forecasts under climate change based on a lumped geo-hydrological model. Journal of Hydrology.
  3. 2022 (submitted)

    1. 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.
    2. Mohammadi F, Eggenweiler E, Flemisch B, Oladyshkin S, Rybak I, Schneider M, u. a. Uncertainty-aware Validation Benchmarks for Coupling Free Flow and Porous-Medium Flow. Water Resources Research.
    3. Schwindt S, Medrano SC, Mouris K, Beckers F, Haun S, Nowak W, u. a. Bayesian calibration indicates overfitting of three-dimensional hydrodynamic reservoir models. Water Resources Research.
    4. Hsueh H-F, Guthke A, Wöhling T, Nowak W. Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions. Water Resources Research.
    5. Oladyshkin S, Praditia T, Kröker 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.
  4. 2022 (accepted)

    1. Schäfer Rodrigues Silva A, Weber TK, Gayler S, Guthke A, Höge M, Streck T, u. a. Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling. Modeling Earth Systems and Environment.
  5. 2022

    1. Ejaz F, Wöhling T, Höge M, Nowak W. Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion. Journal of Hydrology. 2022;606:127347.
    2. Kröker I, Oladyshkin S. Arbitrary Multi-Resolution Multi-Wavelet-based Polynomial Chaos Expansion for Data-Driven Uncertainty Quantification. Reliability Engineering & System Safety. 2022;222:108376.
    3. 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.
    4. Galvan A, Haas J, Moreno-Leiva S, Osorio-Aravena JC, Nowak W, Palma-Behnke R, u. a. Exporting sunshine: Planning South America’s electricity transition with green hydrogen. Applied Energy. 2022;325:119569.
    5. 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
    6. Hsueh H, Guthke A, Wöhling T, Nowak W. Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. Water Resources Research. 2022;58:e2021WR030590.
    7. 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(3):17–30.
    8. Schäfer Rodrigues Silva A, Weber TK, Gayler S, Guthke A, Höge M, Streck T, u. a. Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling. Modeling Earth Systems and Environment. 2022;8:5143–5175.
    9. 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(12).
    10. Zare H, Weber TK, Ingwersen J, Nowak W, Gayler S, Streck T. Combining crop modelling with remote sensing data using a particle filtering technique to produce real-time forecasts of winter wheat yields under uncertain boundary conditions. Remote Sensing. 2022;14:1360.
    11. Banerjee I, Walter P, Guthke A, Mumford KG, Nowak W. The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Computational Geosciences. 2022;
    12. Chavez Rodriguez L, González-Nicolás A, Ingalls B, Nowak W, Xiao S, Pagel H. Optimal design of experiments to improve the characterization of atrazine degradation pathways in soil. European Journal of Soil Science. 2022;73(1):e13211.
    13. Rodriguez-Pretelin A, Morales-Casique E, Nowak W. Optimization-based clustering of random fields for computationally efficient and goal-oriented uncertainty quantification: concept and demonstration for delineation of wellhead protection areas in transient aquifers. Advances in Water Resources. 2022;162:104146.
    14. Wagner A, Sonntag A, Reuschen S, Nowak W, Ehlers W. Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-field model and geostatistics. Proceedings in Applied Mathematics and Mechanics. 2022;6.
    15. 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
    16. Wagner A, Sonntag A, Reuschen S, Nowak W, Ehlers W. Hydraulically induced fracturing in heterogeneous porous media using a TPM-phase-field model and geostatistics. Proceedings in Applied Mathematics and Mechanics. 2022;6.
    17. Xiao S, Nowak W. Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation. Aerospace Science and Technology. 2022;130:107938.
  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.
    2. Moreno-Leiva S, Haas J, Nowak W, Kracht W, Eltrop L, Breyer C. Flexible copper: exploring capacity-based energy demand flexibility in the industry. Applied Energy.
  7. 2021

    1. Cao K-K, Pregger T, Haas J, Lens H. To Prevent or Promote Grid Expansion? Analyzing the Future Role of Power Transmission in the European Energy System. Frontiers in Energy Research [Internet]. Februar 2021;8. Verfügbar unter: https://www.frontiersin.org/articles/10.3389/fenrg.2020.541495/full
    2. Xiao M, Junne T, Haas J, Klein M. Plummeting costs of renewables - Are energy scenarios lagging? Energy Strategy Reviews [Internet]. Mai 2021;35:100636. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S2211467X21000225
    3. Haas J, Prieto L, Ghorbani N, Breyer C. Revisiting the potential of pumped-hydro energy storage: a method to detect economically attractive sites. Renewable Energy. 2021;
    4. Bakhshipour AE, Hespen J, Haghighi A, Dittmer U, Nowak W. Integrating structural resilience in the design of urban drainage networks in flat areas using a simplified multi-objective optimization framework. Water. 2021;13:269.
    5. 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.
    6. González-Inostroza P, Rahmann C, Alvarez R, Haas J, Nowak W, Rehtanz C. The Role of Fast Frequency Response of Energy Storage Systems and Renewables for Ensuring Frequency Stability in Future Low-Inertia Power Systems. Sustainability. 2021;
    7. Manjunath S, Yeligeti M, Fyta M, Haas J, Gils HC. Impact of COVID-19 on electricity demand: Deriving minimum states of system health for studies on resilience. Data. 2021;6(76).
    8. Reuschen S, Guthke A, Nowak W. The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose. Water Resources Research. 2021;57:e2021WR030391.
    9. Xiao S, Xu T, Reuschen S, Nowak W, Franssen H-JH. Bayesian inversion of multi-Gaussian log-conductivity fields with uncertain hyperparameters: an extension of preconditioned Crank-Nicolson Markov chain Monte Carlo with parallel tempering. Water Resources Research. 2021;57:e2021WR030313.
    10. Dibak C, Nowak W, Dürr F, Rothermel K. Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations. IEEE Transactions on Mobile Computing. 2021;
    11. Boyle CFH, Haas J, Kern JD. Development of an irradiance-based weather derivative to hedge cloud risk for solar energy systems. Renewable Energy [Internet]. 2021;164:1230–43. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S0960148120316578
    12. Keller J, Hendricks Franssen H-J, Nowak W. Investigating the Pilot Point Ensemble Kalman Filter for geostatistical inversion and data assimilation. Water Resources Research. 2021;155:104010.
    13. Feldmeyer D, Nowak W, Jamshed A, Birkmann J. An open resilience index: crowdsourced indicators empirically developed from natural hazard and climatic event data. Science of the Total Environment. 2021;774:145734.
    14. Saemiana P, Hosseini-Moghari S-M, Fatehi I, Shoarinezhad V, Modiri E, Tourian MJ, u. a. Comprehensive comparison of precipitation datasets over Iran. Journal of Hydrology. 2021;603:127054.
    15. Moreno-Leiva S, Haas J, Nowak W, Kracht W, Eltrop L, Breyer C. Integration of seawater pumped storage and desalination in multi-energy systems planning: the case of a key material for the energy transition. Applied Energy. 2021;299:117298.
    16. 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. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0306261921000222
    17. Scheurer S, Schäfer Rodrigues Silva A, Mohammadi F, Hommel J, Oladyshkin S, Flemisch B, u. a. Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation. Computational Geosciences. 2021;25:1899–917.
    18. Kiên-Cao K, Haas J, Sperber E, Sasanpour S, Sarfarazi S, Pregger T, u. a. Bridging granularity gaps to decarbonize large-scale energy systems—The case of power system planning. Energy Science & Engineering. März 2021;
    19. Lillo R, Olivares M, Haas J. Grid-wide assessment of varying re-regulation storage capacity for hydropeaking mitigation. Journal of Environmental Management [Internet]. 2021;293:112866. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0301479721009282
    20. Reuschen S, Jobst F, Nowak W. Efficient discretization-independent Bayesian inversion of high-dimensional multi-Gaussian priors using a hybrid MCMC. Water Resources Research. 2021;57(8):e2021WR030051.
    21. González-Nicolás A, Schwientek M, Sinsbeck M, Nowak W. Characterization of export regime in discharge-concentration plots via an advanced time-series model and event-based sampling. Water. 2021;13:1723.
    22. Banerjee I, Guthke A, Van de Ven CJC, Mumford KG, Nowak W. Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data. Water Resources Research. 2021;57(7):e2021WR029986.
    23. Chow R, Parker B, Steelman C, Thoms A, Nowak W. How do fractures influence hyporheic exchange in sedimentary rock riverbeds? Water Resources Research. 2021;57(7):e2020WR028476.
    24. Bakhshipour AE, Dittmer U, Haghighi A, Nowak W. Towards sustainable urban drainage infrastructures planning: a combined multiobjective optimization and multicriteria decision-making platform. Journal of Water Resources Planning and Management. 2021;147(8).
  8. 2020 (accepted)

    1. Haas J, Khalighi J, de la Fuente A, Gerbersdorf S, Nowak W, Chen P-J. Floating photovoltaic plants: ecological impacts versus hydropower operation flexibility. Energy Conversion and Management.
    2. Sinsbeck M, Cook E, Nowak W. Sequential Design of Computer Experiments for the Computation of Bayesian Model Evidence. SIAM Journal of Uncertainty Quantification.
  9. 2020

    1. Haas J, Moreno-Leiva S, Junne T, Chen P-J, Pamparana G, Nowak W, u. a. Copper mining: 100% solar electricity by 2030? Applied Energy [Internet]. März 2020;262(114506). Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0306261920300180
    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. Haas J, Moreno-Leiva S, Junne T, Chen P-J, Pamparana G, Nowak W, u. a. Copper mining: 100% solar electricity by 2030? Applied Energy [Internet]. März 2020;262(114506). Verfügbar unter: https://www.sciencedirect.com/science/article/abs/pii/S0306261920300180
    4. 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(7):e2019WR026966.
    5. Höge M, Guthke A, Nowak W. Bayesian Model Weighting: The Many Faces of Model Averaging. Water [Internet]. 2020;12(2):309. Verfügbar unter: https://www.mdpi.com/2073-4441/12/2/309
    6. Xu T, Reuschen S, Nowak W, Franssen H-JH. Preconditioned Crank-Nicolson Markov chain Monte Carlo coupled with parallel tempering: An efficient method for Bayesian inversion of multi-Gaussian log-hydraulic conductivity fields. Water Resources Research. 2020;56(8):e2020WR027110.
    7. Erdal D, Xiao S, Nowak W, Cirpka O. Sampling Behavioral Model Parameters for Ensemble-based Sensitivity Analysis using Gaussian Process Emulation and Active Subspaces. Stochastic Environmental Research and Risk Assessment. 2020;34:1813–30.
    8. Xiao S, Oladyshkin S, Nowak W. Reliability analysis with stratified importance sampling based on adaptive Kriging. Reliability Engineering & System Safety. 2020;197:106852.
    9. Sinsbeck M, Höge M, Nowak W. Exploratory-phase-free estimation of GP hyperparameters in sequential design methods - at the example of Bayesian inverse problems. Frontiers in Artificial Intelligence, section AI in Food, Agriculture and Water. 2020;3(52):1–16.
    10. Haas J, Khalighi J, de la Fuente A, Gerbersdorf S, Nowak W, Chen P-J. Floating photovoltaic plants: ecological impacts versus hydropower operation flexibility. Energy Conversion and Management. 2020;206:112414.
    11. Moreno-Leiva S, Haas J, Junne T, Valencia F, Godin H, Kracht W, u. a. Renewable energy in copper production: a review on systems design and methodological approaches. Journal of Cleaner Production. Februar 2020;246(118978).
    12. Maier R, González-Nicolás A, Leven C, Nowak W, Cirpka OA. Joint optimization of measurement and modeling strategies with application to radial flow in stratified aquifers. Water Resources Research. 2020;56:e2019WR026872.
    13. Oladyshkin S, Mohammadi F, Kröker I, Nowak W. Bayesian3 active learning for Gaussian process emulator using information theory. Entropy. 2020;22(0890):1–27.
    14. Allgeier J, González-Nicolás A, Erdal D, Nowak W, Cirpka OA. A stochastic framework to optimize monitoring strategies for delineating groundwater divides. Frontiers in Earth Science. 2020;(8):554845.
    15. Manguang G, Zhang L, Miao X, Oladyshkin S, Cheng X, Wang Y, u. a. Application of computed tomography (CT) in geologic CO_2 storage research: a critical review. Journal of Natural Gas Science and Engineering. 2020;103591.
    16. Praditia T, Walser T, Oladyshkin S, Nowak W. Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies [Internet]. 2020;13(15):3873. Verfügbar unter: https://www.mdpi.com/1996-1073/13/15/3873
    17. Reuschen S, Xu T, Nowak W. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC algorithm. Advances in Water Resources. 2020;141:103614.
    18. Schäfer Rodrigues Silva A, Guthke A, Höge M, Cirpka OA, Nowak W. Strategies for simplifying reactive transport models - a Bayesian model comparison. Water Resources Research. 2020;56:e2020WR028100.
    19. Guisandez I, Perez-Diaz JI, Nowak W, Haas J. Should environmental constraints be considered in linear programming based water value calculators? International Journal of Electrical Power & Energy Systems. Mai 2020;117(105662).
    20. Haas J, Khalighi J, de la Fuente A, Gerbersdorf S, Nowak W, Chen P-J. Floating photovoltaic plants: ecological impacts versus hydropower operation flexibility. Energy Conversion and Management. Februar 2020;206(112414).
  10. 2019

    1. 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.
    2. Anindito Y, Haas J, Olivares MA, Kern J, Nowak W. A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs. Journal of Cleaner Production. Februar 2019;210:477–89.
    3. Haas J, Nowak W, Palma-Behnke R. Multi-objective planning of energy storage technologies for a fully renewable system: implications for the main stakeholders in Chile. Energy Policy. März 2019;126:494–506.
    4. González-Nicolás A, Cihan A, Petrusak R, Zhou Q, Trautz R, Riestenberg D, u. a. Pressure management via brine extraction in geological CO2 storage: Adaptive optimization strategies under poorly characterized reservoir conditions. International Journal of Greenhouse Gas Control. 2019;83:176–85.
    5. Ortiz J, Kracht W, Pamparana G, Haas J. Optimization of a SAG mill energy system: integrating rock hardness, solar irradiation, climate change and demand side management. Mathematical Geosciences. Juli 2019;
    6. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part II: effect of ore hardness variability, geometallurgical modeling and demand side management. Minerals Engineering. 2019;137:53–67.
    7. Bode F, Reed P, Reuschen S, Nowak W. Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design. Water Resources Research. 2019;55(3):2257–78.
    8. 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.
    9. Ortiz J, Kracht W, Pamparana G, Haas J. Optimization of a SAG mill energy system: integrating rock hardness, solar irradiation, climate change and demand side management. Mathematical Geosciences. Juli 2019;
    10. J. Salgado, Oladyshkin S, Osmancevic E, Janotte F. Kalibrierung von Rechennetzmodellen anhand probabilistischer Bayes‘scher Verfahren. DWGV energie wasser-praxis. 2019;2:16–21.
    11. Chow R, Hao W, Jeremy B, Jürnjakob D, Wöhling T, Nowak W. Sensitivity of simulated hyporheic exchange to river bathymetry: The Steinlach River Test Site. Groundwater. 2019;57(3):378–91.
    12. Cheng K, Lu Z, Chaozhang K. Gradient-enhanced high dimensional model representation via Bayesian inference. Knowledge-Based Systems. 2019;
    13. Cheng K, Lu Z, Zhen Y. Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression. Computer Methods in Applied Mechanics and Engineering. Juni 2019;349:360–77.
    14. Most S, Bolster D, Bijeljic B, Nowak W. Trajectories as training images to simulate advective-diffusive, non-Fickian transport. Water Resources Research. 2019;55:3465–80.
    15. Höge M, Guthke A, Nowak W. The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination. Journal of Hydrology [Internet]. Mai 2019;572:96–107. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S0022169419301532
    16. Cheng K, Lu Z. Time-variant reliability analysis based on high dimensional model representation. Reliability Engineering & System Safety. August 2019;188:310–9.
    17. Haas J, Hagen D, Nowak W. Energy storage and transmission systems to save the fish? Minimizing hydropeaking for little extra-cost. Sustainable Energy Technologies and Assessments. 2019;35:41–7.
    18. Chow R, Jeremy B, Jürnjakob D, Wöhling T, Nowak W. Evaluating subsurface parameterization to simulate hyporheic exchange: The Steinlach River Test Site. Groundwater. 2019;58(7):93–109.
    19. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part I: framework, model development and effect of solar irradiance forecasting. Minerals Engineering. 2019;137:68–77.
    20. 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
    21. Bakhshipour AE, Bakhshizadeh M, Dittmer U, Haghighi A, Nowak W. Hanging Gardens Algorithm to Generate Decentralized Layouts for Urban Drainage Systems Optimization. Journal of Water Resources Planning and Management. Juni 2019;145(9):04019034.
    22. Wang Y, Xiao S, Lu Z. An efficient method based on Bayes’ theorem to estimate the failure-probability-based sensitivity measure. Mechanical Systems and Signal Processing. 2019;115:607–20.
    23. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part II: effect of ore hardness variability, geometallurgical modeling and demand side management. Minerals Engineering. 2019;
    24. Pamparana G, Kracht W, Haas J, Ortiz JM, Nowak W, Palma-Behnke R. Studying the integration of solar energy into the operation of a semi-autogenous grinding mill. Part I: framework, model development and effect of solar irradiance forecasting. Minerals Engineering. 2019;
    25. Rodriguez-Pretelin A, Nowak W. Dynamic re-distribution of pumping rates in well fields to counter transient problems in groundwater production. Groundwater for Sustainable Development. April 2019;8:606–16.
    26. Bakhshipour AE, Dittmer U, Haghighi A, Nowak W. Hybrid green-blue-gray decentralized urban drainage systems design, a simulation-optimization framework. Journal of Environmental Management. 2019;249:109364.
    27. Motavita DF, Chow R, Guthke A, Nowak W. The Comprehensive Differential Split-Sample Test: A stress-test for hydrological model robustness under climate variability. Journal of Hydrology. 2019;573:501–15.
    28. Oladyshkin S, Nowak W. The connection between Bayesian Inference and Information Theory for model selection, information gain and experimental design. Entropy. 2019;21:1081.
  11. 2018

    1. Wang Y, Xiao S, Lu Z. A new efficient simulation method based on Bayes’ theorem and importance sampling Markov chain simulation to estimate the failure-probability-based global sensitivity. Aerospace Science and Technology. 2018;79:364–72.
    2. Xu T, G’omez-Hernández JJ. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Advances in Water Resources. 2018;112:106–23.
    3. Haas J, Palma-Behnke R, Valencia F, Araya P, D’iaz-Ferrán G, Telsnig T, u. a. Sunset or sunrise? Understanding the barriers and options for the massive deployment of solar technologies in Chile. Energy Policy [Internet]. Januar 2018;112:399–414. Verfügbar unter: http://linkinghub.elsevier.com/retrieve/pii/S0301421517306249
    4. Cheng K, Lu Z. Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression. Computers & Structures. Januar 2018;194:86–96.
    5. 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. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S0309170817311909
    6. Cebulla F, Haas J, Eichman J, Nowak W, Mancarella P. How much Electrical Energy Storage do we need? A synthesis for the U.S., Europe, and Germany. Journal of Cleaner Production [Internet]. April 2018;181:449–59. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0959652618301665
    7. Xiao S, Lu Z, Wang P. Global sensitivity analysis based on distance correlation for structural systems with multivariate output. Engineering Structures. 2018;167:74–83.
    8. Haas J, Cebulla F, Nowak W, Rahmann C, Palma R. A multi-service approach for planning the optimal mix of energy storage technologies in a fully renewable power supply. Energy Conversion and Management [Internet]. 2018;178:355–68. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0196890418310987
    9. Darscheid P, Guthke A, Ehret U. A Maximum-Entropy Method to Estimate Discrete Distributions  from Samples Ensuring Nonzero Probabilities. Entropy [Internet]. 2018;20(8). Verfügbar unter: http://www.mdpi.com/1099-4300/20/8/601
    10. Höge M, Wöhling T, Nowak W. A Primer for Model Selection: The Decisive Role of  Model Complexity. Water Resources Research [Internet]. 2018; Verfügbar unter: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021902
    11. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy      boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources. 2018;115:160–71.
    12. Xiao S, Lu Z. Global sensitivity analysis based on Gini’s mean difference. Structural and Multidisciplinary Optimization. 2018;58(4):1523–35.
    13. Xiao S, Lu Z, Wang P. Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition. Risk Analysis. Dezember 2018;38(12):2703–21.
    14. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy      boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources. 2018;
    15. Mehne J, Nowak W. Predicting the battery core temperature: explanatory power of  measurement quantities under different uncertainty scenarios. Journal of Energy Storage. 2018;18:476–84.
    16. Rodriguez-Pretelin A, Nowak W. Integrating transient behavior as a new dimension to WHPA delineation. Advances in Water Resources. 2018;119:178–87.
    17. Praditia T, Helmig R, Hajibeygi H. Multiscale formulation for coupled flow-heat equations arising from single-phase flow in fractured geothermal reservoirs. Computational Geosciences [Internet]. Oktober 2018;22(5):1305–22. Verfügbar unter: https://doi.org/10.1007/s10596-018-9754-4
    18. Xiao S, Lu Z. Reliability analysis by combining higher-order unscented transformation and fourth-moment method. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 2018;4(1):04017034.
    19. Wang P, Lu Z, Xiao S. Variance-based sensitivity analysis with the uncertainties of the input variables and their distribution parameters. Communication in Statistics-Simulation and Computation. 2018;47(4):1103–25.
    20. Cheng K, Lu Z. Sparse polynomial chaos expansion based on D-MORPH regression. Applied Mathematics and Computation. April 2018;323:17–30.
    21. Chen Z, G’omez-Hernández JJ, Xu T, Zanini A. Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart ensemble Kalman filter. Journal of Hydrology. 2018;564:1074–84.
    22. Oladyshkin S, Nowak W. Incomplete statistical information limits the utility of higher-order polynomial chaos expansions. Reliability Engineering & System Safety. 2018;169:137–48.
    23. Bode F, Ferre T, Zigelli N, Emmert M, Nowak W. Reconnecting Stochastic Methods with Hydrogeological Applications: A Utilitarian Uncertainty Analysis and Risk Assessment Approach for the Design of Optimal monitoring Networks. Water Resources Research. 2018;54:2270–97.
    24. Xiao S, Lu Z, Wang P. Multivariate global sensitivity analysis for dynamic models based on energy distance. Structural and Multidisciplinary Optimization. 2018;57(1):279–91.
    25. Wang P, Lu Z, Zhang K, Xiao S, Yue Z. Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables. Reliability Engineering & System Safety. 2018;169:437–50.
    26. Rahmann C, Mayol C, Haas J. Dynamic control strategy in partially-shaded photovoltaic power plants for improving the frequency of the electricity system. Journal of Cleaner Production [Internet]. 2018;202C:109–19. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0959652618322996?via%3Dihub
    27. Sun AY, González-Nicolás A, Jeong H, Templeton TC. Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning. Computers & Geosciences [Internet]. 2018;113(https://doi.org/10.1016/j.cageo.2018.01.006):70–80. Verfügbar unter: https://www.sciencedirect.com/science/article/pii/S0098300417305393
  12. 2017 (submitted)

    1. Gosses M, Nowak W, Wöhling T. Explicit treatment for Dirichlet, Neumann and Cauchy boundary conditions in POD-based reduction of groundwater models. Advances in Water Resources.
  13. 2017

    1. González-Nicolás A, Trevisan L, Illangasekare TH, Cihan A, Birkholzer JT. Enhancing capillary trapping effectiveness through proper time scheduling of injection of supercritical CO2 in heterogeneous formations. Greenhouse Gases: Science and Technology [Internet]. 2017;7(2):339–52. Verfügbar unter: http://dx.doi.org/10.1002/ghg.1646
    2. Carpentier D, Haas J, Olivares M, de la Fuente A. Modeling the Multi-Seasonal Link between the Hydrodynamics of a Reservoir and Its Hydropower Plant Operation. Water [Internet]. Mai 2017;9(6):367. Verfügbar unter: http://www.mdpi.com/2073-4441/9/6/367
    3. Wirtz D, Nowak W. The rocky road to universal scientific simulation frameworks. Environmental Software and Modelling. 2017;93:180–92.
    4. Xiao S, Lu Z, Qin F. Estimation of the generalized Sobol’s sensitivity index for multivariate output model using unscented transformation. Journal of Structural Engineering. 2017;143(5):06016005.
    5. Wirtz D, Nowak W. The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control. Environmental Software and Modelling. 2017;93:180–92.
    6. Moreno-Leiva S, D’iaz-Ferrán G, Haas J, Telsnig T, D’iaz-Alvarado FA, Palma-Behnke R, u. a. Towards solar power supply for copper production in Chile: Assessment of global warming potential using a life-cycle approach. Journal of Cleaner Production [Internet]. Oktober 2017;164:242--249. Verfügbar unter: http://linkinghub.elsevier.com/retrieve/pii/S0959652617312076
    7. Haas J, Cebulla F, Karl-Kiên C, Nowak W, Palma-Behnke R, Rahmann C, u. a. Challenges and trends of energy storage expansion planning for flexibility provision in power systems - a review. Renewable & Sustainable Energy Reviews [Internet]. Dezember 2017;80:603–619. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S1364032117308377
    8. Guthke A. Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice. Groundwater [Internet]. 2017;55(5). Verfügbar unter: http://dx.doi.org/10.1111/gwat.12554
    9. 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.
    10. 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(2):270–83.
    11. Emmert M, Zigelli N, Haakh F, Bode F, Nowak W. Risikobasiertes Grundwassermonitoring für Wasserschutzgebiete. energie | wasser-praxis. 2017;67(8):68–71.
    12. Xiao S, Lu Z, Xu Liyang. Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis. Reliability Engineering & System Safety. 2017;165:1–10.
    13. Xiao S, Lu Z. Structural reliability sensitivity analysis based on classification of model output. Aerospace Science and Technology. 2017;71:52–61.
    14. Cheng K, Lu Z, Wei Y, Shi Y, Zhou Y. Mixed kernel support vector regression for global sensitivity analysis. Mechanical Systems and Signal Processing. November 2017;96:201–14.
    15. 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.
    16. Trevisan L, Pini R, Cihan A, Birkholzer JT, Zhou Q, González-Nicolás A, u. a. Imaging and quantification of spreading and trapping of carbon dioxide in saline aquifers using meter-scale laboratory experiments. Water Resources Research [Internet]. 2017;53(1):485–502. Verfügbar unter: http://dx.doi.org/10.1002/2016WR019749
    17. Bode F, Nowak W, Emmert M, Zigelli N. Optimale Grundwassermessstellennetze: Multi-kriterielle  Optimierung als Entscheidungshilfe. gwf Wasser+Abwasser. 2017;158(07–08):111–21.
    18. González-Nicolás A, Baú D, Cody BM. Application of binary permeability fields for the study of CO2 leakage from geological carbon storage in saline aquifers of the Michigan Basin,. Mathematical Geosciences [Internet]. 25. Oktober 2017; Verfügbar unter: https://doi.org/10.1007/s11004-017-9706-x
    19. Pamparana G, Kracht W, Haas J, D’iaz-Ferrán G, Palma-Behnke R, Román R. Integrating photovoltaic solar energy and a battery energy storage system to operate a semi-autogenous grinding mill. Journal of Cleaner Production [Internet]. November 2017;165:273–80. Verfügbar unter: http://linkinghub.elsevier.com/retrieve/pii/S0959652617315536
    20. Mehne J, Nowak W. Improving temperature predictions for Li-ion batteries: data assimilation with a stochastic extension of a physically-based, thermo-electrochemical model. Journal of Energy Storage [Internet]. 2017;12:288–96. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S2352152X16302328
    21. Cheng K, Lu Z, Zhou Y, Shi Y, Wei Y. Global sensitivity analysis using support vector regression. Applied Mathematical Modelling. September 2017;49:587–98.
    22. Sinsbeck M, Nowak W. Sequential Design of Computer Experiments for the Solution of Bayesian Inverse Problems. SIAM /ASA Journal of Uncertainty Quantification. 2017;5(1):640–64.
    23. Cihan A, Birkholzer JT, Trevisan L, González-Nicolás A, Illangasekare T. Investigation of representing hysteresis in macroscopic models of two-phase flow in porous media using intermediate scale experimental data. Water Resources Research [Internet]. 2017;53(1):199–221. Verfügbar unter: http://dx.doi.org/10.1002/2016WR019449
    24. Wang P, Lu Z, Xiao S. A generalized separation for the variance contributions of input variables and their distribution parameters. Applied Mathematical Modelling. 2017;47:381–99.
  14. 2016

    1. Koch J, Nowak W. Identification of contaminant source architectures - A statistical inversion that emulates multi-phase physics in a computationally practicable manner. Water Resources Research. 2016;52(2):1009–25.
    2. Xiao S, Lu Z. Structural reliability analysis using combined space partition technique and unscented transformation. Journal of Structural Engineering. 2016;142(11):04016089.
    3. Rahmann C, Vittal V, Ascui J, Haas J. Mitigation Control against Partial Shading Effects in Large-scale PV Power Plants. IEEE Transactions on Sustainable Energy. 2016;7(1):173–80.
    4. Xu T, G’omez-Hernández JJ. Characterization of non-Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures. Water Resources Research. 2016;52(8):6111–36.
    5. Lötgering-Lin O, Schöniger A, Nowak W, Gross J. Bayesian Model Selection helps to Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity-Model based on Entropy Scaling. Industrial & Engineering Chemistry Research. 2016;55(38):10191–207.
    6. 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.
    7. Schulte DO, Rühaak W, Oladyshkin S, Welsch B, Sass I. Optimization of Medium Deep Borehole Thermal Energy Storages. Energy Technology. 2016;(4):104–13.
    8. Wöhling T, Geiges A, Nowak W. Optimal design of multi-type groundwater monitoring networks using easily accessible tools. Groundwater. 2016;54(6):861–70.
    9. Birkmann J, Wenzel F, Greiving S, Garschagen M, Vall’ee D, Nowak W, u. a. Extreme Events, Critical Infrastructures, Human Vulnerability and Strategic Planning: Emerging Research Issues. Journal of Extreme Events. 2016;3(2):1650017(1-25).
    10. 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.
    11. Chow R, Frind ME, Frind EO, Jones JP, Sousa MR, Rudolph DL, u. a. Delineating Baseflow Contribution Areas for Streams – A Model and Methods Comparison. Journal of Contaminant Hydrology. 195:11–22.
    12. Most S, Bijeljic B, Nowak W. Evolution and persistence of cross-directional statistical dependence during finite-P’eclet transport through a real porous medium. Water Resources Research. 2016;52(11):8920–37.
    13. Xu T, G’omez-Hernández JJ. Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering. Water Resources Research. 2016;52(8):6587–95.
    14. Nowak W, Guthke A. Entropy-based experimental design for optimal model discrimination in the geosciences. Entropy. 2016;18(11):409.
    15. Flemisch B, Nordbotten JM, Nowak W, Raoof A. Special Issue on „‚NUPUS: Non-linearities and Upscaling in PoroUS Media‘“ (Editorial). Transport in Porous Media. 2016;114:237–40.
    16. Vereecken H, Schnepf A, Hopmans JW, Or MJD, Roose T, Vanderborght J, u. a. Modeling Soil Processes: Key challenges and new perspectives. Vadoze Zone Journal. 15(5).
    17. Xiao S, Lu Z, Xu Liyang. A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty. Reliability Engineering & System Safety. 2016;156:1–14.
    18. 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
  15. 2015

    1. Xu T, G’omez-Hernández JJ. Inverse sequential simulation: Performance and implementation details. Advances in water resources. 2015;86:311–26.
    2. Xu T, G’omez-Hernández JJ. Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non-Gaussian field. Water Resources Research. 2015;51(4):2227–42.
    3. Cody BM, Baú D, González-Nicolás A. Stochastic injection-strategy optimization for the preliminary assessment of candidate geological storage sites. Hydrogeology Journal. 2015;23(6):1229–45.
    4. Baú D, Cody BM, González-Nicolás A. An iterative global pressure solution for the semi-analytical simulation of geological carbon sequestration. Computational Geosciences. 2015;19(4):781–9.
    5. Nowak W, Bode F, Loschko M. A multi-objective optimization concept for risk-based early-warning monitoring networks in well catchments. Procedia Environmental Sciences. 2015;25:191–8.
    6. Enzenhöfer R, Nowak W, Binning PJ. Stakeholder-Objective Risk Model (STORM): Determining the aggregated risk of multiple contaminant hazards in groundwater well catchments. Advances in Water Resources. 2015;83:165–75.
    7. Wöhling T, Schöniger A, Gayler S, Nowak W. Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction. Water Resources Research. 2015;51(4):2825–46.
    8. Koch J, Nowak W. Predicting DNAPL mass discharge and contaminated site longevity probabilities: Conceptual model and high-resolution stochastic simulation. Water Resources Research. 2015;51(2):806–31.
    9. Bode F, Nowak W, Loschko M. Optimization for early-warning monitoring networks in well catchments should be multi-objective, risk-prioritized and robust against uncertainty. Transport in Porous Media. 2015;114:261–81.
    10. González-Nicolás A, Baú D, Cody BM, Alzraiee A. Stochastic and global sensitivity analyses of uncertain parameters affecting the safety of geological carbon storage in saline aquifers of the Michigan Basin. International Journal of Greenhouse Gas Control. 2015;37:99–114.
    11. Geiges A, Rubin Y, Nowak W. Interactive design of experiments: A priori global versus sequential optimization, revised under changing states of knowledge. Water Resources Research. 2015;51(10):7915–36.
    12. Haas J, Olivares MA, Palma-Behnke R. Grid-wide subdaily hydrologic alteration under massive wind power penetration in Chile. Journal of Environmental Management [Internet]. Mai 2015;154:183–9. Verfügbar unter: http://linkinghub.elsevier.com/retrieve/pii/S0301479715000857
    13. González-Nicolás A, Baú D, Alzraiee A. Detection of potential leakage pathways from geological carbon storage by fluid pressure data assimilation. Advances in Water Resources. 2015;86:366–84.
    14. Schöniger A, Illman W, Wöhling T, Nowak W. Finding the Right Balance Between Groundwater Model Complexity and Experimental Effort via Bayesian Model Selection. Journal of Hydrology. 2015;531(1):96–110.
    15. Olivares MA, Haas J, Palma-Behnke R, Benavides C. A framework to identify Pareto-efficient subdaily environmental flow constraints on hydropower reservoirs using a grid-wide power dispatch model. Water Resources Research [Internet]. Mai 2015;51(5):3664–80. Verfügbar unter: http://doi.wiley.com/10.1002/2014WR016215
    16. Schöniger A, Wöhling T, Nowak W. A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking. Water Resources Research. 2015;51(9):7524–46.
    17. Sinsbeck M, Nowak W. An optimal sampling rule for nonintrusive polynomial chaos expansions of expensive models. International Journal for Uncertainty Quantification. 2015;5(3):275–95.
    18. Xu T, G’omez-Hernández JJ. Probability fields revisited in the context of ensemble Kalman filtering. Journal of Hydrology. 2015;531:40–52.
    19. Gerbersdorf SU, Cimatoribus C, Class H, Engesser KH, Helbich S, Hollert H, u. a. Anthropogenic Trace Compounds (ATCs) in aquatic habitats - research needs on sources, fate, detection and toxicity to ensure timely elimination strategies and risk management. Environment International. 2015;79:85–105.
    20. Sinsbeck M, Tartakovsky D. Impact of data assimilation on cost-accuracy tradeoff in multifidelity models. SIAM/ASA Journal of Uncertainty Quantification. 2015;3(1):954–68.
  16. 2014

    1. Koch J, Nowak W. A method for implementing Dirichlet and third-type boundary conditions in PTRW simulations. Water Resources Research. 2014;50(2):1374–95.
    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. 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
    4. 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(5):1065–80.
    5. Schöniger Anneli, Wöhling T, Samaniego L, Nowak W. Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence. Water Resources Research. 2014;50(12):9484–513.
    6. Enzenhöfer R, Bunk T, Nowak W. Nine steps to risk-informed wellhead protection and management: A case study. Groundwater. 2014;52:161–74.
  17. 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(4):671–87.
    2. Wöhling T, Geiges A, Nowak W, Gayler S. Towards optimizing experiments for maximum-confidence model selection between different soil-plant models. Procedia Environmental Sciences. 2013;19:514–23.
    3. Grathwohl P, Rügner H, Wöhling T, Osenbrück K, Schwientek M, Gayler S, u. a. Catchments as Reactors: A comprehensive approach for water fluxes and solute turn-over            (Introduction to the Thematic Issue on Catchment Research). Environmental Earth Science. 2013;69(2):317–33.
    4. Xu T, G’omez-Hernández JJ, Li L, Zhou H. Parallelized ensemble Kalman filter for hydraulic conductivity characterization. Computers & geosciences. 2013;52:42–9.
    5. Leube P, de Barros FPJ, Nowak W, Rajagopal R. Towards optimal allocation of computer resources: trade-offs between uncertainty quantification,discretization and model reduction. Environmental Software and Modelling. 2013;50:97–107.
    6. Nowak W, Litvinenko A. Kriging and spatial design accelerated by orders of magnitude: combining low-rank covariance approximations with FFT-techniques. Mathematical Geosciences. 2013;45(4):411–35.
    7. Grathwohl P, Rügner H, Wöhling T, Osenbrück K, Schwientek M, Gayler S, u. a. Catchments as Reactors: A comprehensive approach for water fluxes and solute turn-over (Introduction to the Thematic Issue on Catchment Research). Environmental Earth Science. 2013;69(2):317–33.
    8. Oladyshkin S, Schröder P, Class H, Nowak W. Chaos Expansion based Bootstrap Filter to Calibrate CO2 Injection Models. Energy Procedia. 2013;40:398–407.
    9. Xu T, G’omez-Hernández JJ, Zhou H, Li L. The power of transient piezometric head data in inverse modeling: An application of the localized normal-score EnKF with covariance inflation in a heterogenous bimodal hydraulic conductivity field. Advances in water resources. 2013;54:100–18.
    10. 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.
  18. 2012

    1. Schöniger A, Nowak W, Franssen HJH. Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography. Water Resources Research. 2012;48(W04502).
    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. Leube P, Geiges A, Nowak W. Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design. Water Resources Research. 2012;48(W02501).
    5. Nowak W, Rubin Y, de Barros FPJ. A hypothesis-driven approach to optimal site investigation. Water Resources Research. 2012;48(W06509).
    6. Oladyshkin S, Nowak W. Polynomial Response Surfaces for Probabilistic Risk Assessment and Risk Control via Robust Design (Book). Luo Y, Herausgeber. Novel Approaches and Their Applications in Risk Assessment, ISBN: 978-953-51-0519-0 [Internet]. 2012; Verfügbar unter: /brokenurl#www.intechopen.com/books/
    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. 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
    9. de Barros FPJ, Dentz M, Koch J, Nowak W. Flow topology and scalar mixing in heterogeneous porous media. Geophysical Research Letters. 2012;39(L08404).
    10. Cirpka OA, Rolle M, Chiogna G, de Barros FPJ, Nowak W. Stochastic Evaluation of Mixing-Controlled Steady-State Plume Lengths in Two-Dimensional Heterogeneous Domains. Journal of Contaminant Hydrology. 2012;138–139:22–39.
    11. Tartakovsky D, Nowak W, Bolster D. Introduction to the special issue on uncertainty quantification and risk assessment. Advances in Water Resources. 2012;36:1–2.
    12. 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.
    13. Enzenhöfer R, Nowak W, Helmig R. Probabilistic Exposure Risk Assessment with Advective-Dispersive Well Vulnerability Criteria. Advances in Water Resources. 2012;36:121–32.
    14. Leube P, Nowak W, Schneider G. Temporal Moments revisited: Why there is there nobetter way for physically-based model reduction in time. Water Resources Research. 2012;48(W11527).
    15. Troldborg M, Nowak W, Lange I, Santos M, Binning P, Bjerg PL. Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites. Water Resources Research. 2012;48(W09535).
  19. 2011

    1. Hlawatsch M, Leube P, Nowak W, Weiskopf D. Flow Radar Glyphs-Static Visualization of Unsteady Flow with Uncertainty. IEEE Transactions on Visualization and Computer Graphics. 2011;17(12):1949–58.
    2. Cirpka OA, de Barros FPJ, Chiogna G, Rolle M, Nowak W. Stochastic Flux-Related Analysis of Transverse Mixing in Two-Dimensional Heterogeneous Porous Media. Water Resources Research. 2011;47(W06515).
    3. Oladyshkin S, Panfilov M. Hydrogen penetration in water through porous medium: application to a radioactive waste storage site. Environmental Earth Sciences. 2011;64(4):989–99.
    4. 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.
    5. 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(3):565–77.
    6. Cirpka OA, de Barros FPJ, Chiogna G, Nowak W. Probability Density Function of Steady-State Concentration in Two-Dimensional    Heterogeneous Porous Media. Water Resour Res. 2011;47(W11523).
    7. Cirpka OA, de Barros FPJ, Chiogna G, Nowak W. Probability Density Function of Steady-State Concentration in Two-Dimensional Heterogeneous Porous Media. Water Resources Research. 2011;47(W11523).
    8. Cheng T, Wang Q, Ji X, Huang L, Xu T. Parallel computing method for groundwater flow simulation in porous media. Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications). 2011;47(20):234–7.
    9. de Barros FPJ, Bolster D, Sanchez-Vila X, Nowak W. A Divide and Conquer Approach to Cope with Uncertainty, Human Health Risk and Decision Making in Contaminant Hydrology. Water Resources Research. 2011;47(W05508).
    10. 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.
  20. 2010

    1. Troldborg M, Nowak W, Tuxen N, Bjerg PL, Helmig R, Binning PJ. Uncertainty evaluation of mass discharge estimates from a contaminated site using a fully Bayesian framework. Water Resources Research. 2010;46(12).
    2. de Barros FPJ, Nowak W. On the Link Between Contaminant Source Release Conditions and Plume Prediction Uncertainty. Journal of Contaminant Hydrology. 2010;116:24–34.
    3. Nowak W, de Barros FPJ, Rubin Y. Bayesian Geostatistical Design: Task-Driven Optimal Site Investigation when the Geostatistical Model is Uncertain. Water Resources Research. 2010;46(W03535).
    4. Nowak W. Measures of Parameter Uncertainty in Geostatistical Estimation and Geostatistical Optimal Design. Mathematical Geosciences. 2010;42(2):199–221.
  21. 2009

    1. Nowak W. Best unbiased ensemble linearization and the Quasi-Linear Kalman Ensemble Generator. Water Resources Research. 2009;45(W04431).
    2. Troldborg M, Nowak W, Tuxen N, Bjerg PL, Helmig R, Binning P. Quantifying uncertainty in mass discharge estimates from contaminated sites using a fully Bayesian framework. ModelCARE: Calibration and Reliability in Groundwater Modeling - Managing Groundwater and the Environment (20 - 23 September 2009, Wuhan, China) China University of Geosciences, 9/2009. 2009;
    3. Fritz J, Nowak W, Neuweiler I. Application of FFT-based Algorithms for Large-Scale Universal Kriging Problems. Mathematical Geosciences. 2009;51(5):199–221.
  22. 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(3):311–29.
    2. Nowak W, Schwede RL, Cirpka OA, Neuweiler I. Probability density functions of hydraulic head and velocity in three-dimensional heterogeneous porous media. Water Resources Research. 2008;44(W08452).
    3. Schwede RL., Cirpka OA, Nowak W, Neuweiler I. Impact of sampling volume on the probability density function of steady state concentration. Water Resources Research. 2008;44(W12433).
    4. Vasin M, Lehmann P, Kaestner A, Hassanein R, Nowak W, Neuweiler I. Drainage in Heterogeneous Sand Columns with Different Geometric Structures. Advances in Water Resources. 2008;31(9):1205–20.
  23. 2007

    1. Oladyshkin S, Panfilov M. Limit thermodynamic model for compositional gas-liquid systems moving in a porous medium. Transport in Porous Media. 2007;70(2):147–65.
    2. 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(2):137–46.
    3. 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(1):7–12.
  24. 2006

    1. Nowak W, Cirpka OA. Geostatistical Inference of Hydraulic Conductivity and Dispersivities from Hydraulic Heads and Tracer Data. Water Resources Research. 2006;42(W08416).
  25. 2005

    1. Li W, Nowak W, Cirpka OA. Geostatistical inverse modeling of transient pumping tests using temporal moments of drawdown. Water Resources Research. 2005;41(W08403).
    2. Rahman MA, Jose SC, Nowak W, Cirpka OA. Experiments on vertical transverse mixing in a large-scale heterogeneous model aquifer. Journal of Contaminant Hydrology. 2005;80(3):130–48.
    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.
  26. 2004

    1. Cirpka OA, Nowak W. First-order variance of travel time in non-stationary formations. Water Resources Research. 2004;40(W03507).
    2. Cirpka OA, Bürger CM, Nowak W, Finkel M. Uncertainty and data worth analysis for the hydraulic design of funnel-and-gate systems in heterogeneous aquifers. Water Resources Research. 2004;40(W11502).
    3. Nowak W, Cirpka OA. A modified Levenberg-Marquardt Algorithm for Quasi-linear Geostatistical Inversing. Advances in Water Resources. 2004;27(7):737–50.
  27. 2003

    1. Cirpka OA, Nowak W. Dispersion on kriged hydraulic conductivity fields. Water Resources Research. 2003;39(2):1027.
    2. Nowak W, Tenkleve S, Cirpka OA. Efficient computation of linearized cross-covariance and auto-covariance matrices of interdependent quantities. Mathematical Geology. 2003;35(1):53–66.
    3. 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.
  28. 2002

    1. 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.
  29. 2001

    1. 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.
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