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2A composite metric for evaluating system resilience with non-idealistic performance curves
M. Yeligeti, H. Gils, and W. Nowak. PLOS ONE, (2025 (submitted))
a month ago by @pumaiws
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2An estimation method for real-time failure probability of a cascade dam group utilising monitoring data and ordered entropy
J. Wang, Q. Zhong, W. Nowak, D. Li, and Y. Shan. Structural Safety, (2024 (submitted))
a month ago by @pumaiws
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2Reliability sensitivity analysis with multiple failure domains based on an extended two-stage Markov chain Monte Carlo simulation
S. Xiao, and W. Nowak. Stochastic Environmental Research and Risk Assessment, (2025 (submitted))
a month ago by @pumaiws
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2Bayesian Failure Localization Identifies Inconsistencies between Water Distribution Network Models and Real-World Conditions
G. Köse, J. Zamora, E. Osmancevic, F. Janotte, S. Oladyshkin, and W. Nowak. Journal of Water Resources Planning and Management, (2025 (accepted))
a month ago by @pumaiws
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2Efficient Inference for Non-Deterministic Fractures
T. Bruennette, and W. Nowak. geoENV2024 Book of Abstracts, page 67--68. Chania, Crete, GR, International Association of Hydrogeology – Spanish Chapter, Creative Commons Licence BY-NC-ND 4.0, (June 2024)
a month ago by @pumaiws
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3Inferring Underwater Topography with Finite Volume Neural Networks
C. Horuz, M. Karlbauer, T. Praditia, S. Oladyshkin, W. Nowak, and S. Otte. ESANN 2025 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence andMachine Learning, European Symposium on Artificial Neural Networks, (2025)
a month ago by @pumaiws
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2IWSABT:ls3IWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSAUTHOR:PraditiaTimothyIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2025
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2A framework for objectively comparing competing invasion percolation models based on highly-resolved image data
I. Banerjee, A. Guthke, C. Van de Ven, K. Mumford, and W. Nowak. PLOS ONE, (2025 (submitted))
a month ago by @pumaiws
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2HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time
L. Pawusch, S. Scheurer, W. Nowak, and R. Maxwell. Advances in Water Resources, (may 2025 (submitted))
3 months ago by @pumaiws
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IWSAUTHOR:NowakWolfgang
IWSAUTHOR:PawuschLouisa
IWSAUTHOR:ScheurerStefania
IWSMONTH:05
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1IWSABT:ls3IWSAUTHOR:NowakWolfgangIWSAUTHOR:PawuschLouisaIWSAUTHOR:ScheurerStefaniaIWSMONTH:05IWSSUBTYPE:articleIWSTYPEDE:PublikationenIWSTYPEEN:articlesIWSTYPENR:1IWSYEAR:2025
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2Degradation of bacteria for water purification in a TiO2-coated photocatalytic reactor illuminated by solar light
L. Dufner, P. Hofmann, D. Dobslaw, and F. Kern. Applied Water Science, (April 2025)
3 months ago by @pumaiws
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IWSAUTHOR:HofmannPhilipp
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2Immobilization of per- and polyfluoroalkyl substances (PFAS) : experimental and model-based analysis of leaching behavior
T. Bierbaum. Universität Stuttgart, Stuttgart, Dissertation, (2024)
4 months ago by @pumaiws
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IWSAUTHOR:BierbaumThomas
IWSEXAMINER:HaslauerClaus
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IWSMONTH:11
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IWSABT:vegasIWSAUTHOR:BierbaumThomasIWSEXAMINER:HaslauerClausIWSHEFT:314IWSMONTH:11IWSSUBTYPE:phdthesisIWSTYPEDE:Doktorarbeiten_HabitilationenIWSTYPEEN:phd_and_habilitation_thesesIWSTYPENR:8IWSYEAR:2024
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2Evaluation of Thiobacillus denitrificans' sustainability in nitrate-reducing Fe(II) oxidation and the potential significance of Fe(II) as a growth-supporting reductant
S. Becker, T. Dang, R. Wei, and A. Kappler. FEMS Microbiol. Ecol., (April 2025)
4 months ago by @pumaiws
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1IWSABT:ls3IWSAUTHOR:WeiRanIWSMONTH:04IWSSUBTYPE:articleIWSTYPEDE:PublikationenIWSTYPEEN:articlesIWSTYPENR:1IWSYEAR:2025
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2Large Temperatures in Water Distribution Pipes as a Water Quality Threat: Measurements and Modelling
C. Haslauer, I. Kroeker, E. Nißler, S. Oladyshkin, W. Nowak, H. Class, and E. Osmancevic. Geophys. Res. Abstr., EGU25-13131, EGU General Assembly 2024, (April 2025)
5 months ago by @pumaiws
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2IWSABT:ls3IWSAUTHOR:KroekerIljaIWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2025
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2Bayesian3 Active Learning for Regularized Multi-Resolution Arbitrary Polynomial Chaos using Information Theory
I. Kröker, T. Brünnette, N. Wildt, M. Oreamuno, R. Kohlhaas, S. Oladyshkin, and W. Nowak. International Journal for Uncertainty Quantification, 15 (3): 21--54 (January 2025)
7 months ago by @pumaiws
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IWSAUTHOR:BruennetteTim
IWSAUTHOR:KohlhaasRebecca
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IWSAUTHOR:MoralesOreamunoMariaFernanda
IWSAUTHOR:NowakWolfgang
IWSAUTHOR:OladyshkinSergey
IWSAUTHOR:WildtNils
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1IWSABT:ls3IWSAUTHOR:BruennetteTimIWSAUTHOR:KohlhaasRebeccaIWSAUTHOR:KroekerIljaIWSAUTHOR:MoralesOreamunoMariaFernandaIWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSAUTHOR:WildtNilsIWSMONTH:01IWSSUBTYPE:articleIWSTYPEDE:PublikationenIWSTYPEEN:articlesIWSTYPENR:1IWSYEAR:2025
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2Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time
L. Pawusch, S. Scheurer, W. Nowak, and R. Maxwell. Geophys. Res. Abstr., 26, EGU2025-10229, EGU General Assembly 2025, (April 2025)
7 months ago by @pumaiws
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IWSAUTHOR:NowakWolfgang
IWSAUTHOR:PawuschLouisa
IWSAUTHOR:ScheurerStefania
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2IWSABT:ls3IWSAUTHOR:NowakWolfgangIWSAUTHOR:PawuschLouisaIWSAUTHOR:ScheurerStefaniaIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2025
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2Surrogate-assisted Bayesian inference with ERT data for contaminant transport modelling in the subsurface
M. Morales Oreamuno, N. Menzel, S. Oladyshkin, F. Wagner, and W. Nowak. Geophys. Res. Abstr., 26, EGU25-12561, EGU General Assembly 2025, (April 2025)
7 months ago by @pumaiws
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IWSAUTHOR:MoralesOreamunoMariaFernanda
IWSAUTHOR:NowakWolfgang
IWSAUTHOR:OladyshkinSergey
IWSMONTH:04
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IWSTYPEDE:Konferenzbeiträge
IWSTYPEEN:contribution_to_edited_volumes
IWSTYPENR:3
IWSYEAR:2025
2IWSABT:ls3IWSAUTHOR:MoralesOreamunoMariaFernandaIWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2025
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2Modeling the Ammonium Removal Processes in Household Sand Filters
R. Wei, A. Le, B. Liu, M. Azari, W. Nowak, A. Kappler, and S. Oladyshkin. Geophys. Res. Abstr., 26, EGU25-205, EGU General Assembly 2025, (April 2025)
7 months ago by @pumaiws
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IWSAUTHOR:OladyshkinSergey
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2IWSABT:ls3IWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSAUTHOR:WeiRanIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2025
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2Which level of model complexity is justified by your data? A Bayesian answer
A. Schöniger, W. Illman, T. Wöhling, and W. Nowa. Geophys. Res. Abstr., 18, EGU2016-12413, EGU General Assembly 2016, (April 2016)
7 months ago by @pumaiws
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IWSAUTHOR:GuthkeAnneli
IWSAUTHOR:NowakWolfgang
IWSMONTH:04
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2IWSABT:ls3IWSAUTHOR:GuthkeAnneliIWSAUTHOR:NowakWolfgangIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2016
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6Let’s move to spheres! Why a spherical coordinate system is rewarding when analyzing particle increment statistics
S. Most, W. Nowak, and B. Bijeljic. Geophys. Res. Abstr., 18, EGU2016-17391-1, EGU General Assembly 2016, (April 2016)
7 months ago by @pumaiws
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IWSAUTHOR:MostSebastian
IWSAUTHOR:NowakWolfgang
IWSMONTH:04
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IWSTYPEEN:contribution_to_edited_volumes
IWSTYPENR:3
IWSYEAR:20216
2IWSABT:ls3IWSAUTHOR:MostSebastianIWSAUTHOR:NowakWolfgangIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:20216
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3Uncertainty quantification for a hydro-morphodynamic model of river Rhine
T. Mai, W. Nowak, R. Kopmann, and S. Oladyshkin. Geophys. Res. Abstr., 18, EGU2016-5701-3, EGU General Assembly 2016, (April 2016)
7 months ago by @pumaiws
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IWSAUTHOR:NowakWolfgang
IWSAUTHOR:OladyshkinSergey
IWSMONTH:04
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IWSTYPEEN:contribution_to_edited_volumes
IWSTYPENR:3
IWSYEAR:2016
2IWSABT:ls3IWSAUTHOR:NowakWolfgangIWSAUTHOR:OladyshkinSergeyIWSMONTH:04IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2016
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2Lumped hydrological model for reasonable, long- term predictions of groundwater storage and depletion
F. Ejaz, T. Wöhling, and W. Nowak. Geophys. Res. Abstr., EGU2020-1310, EGU General Assembly 2020, (May 2020)
7 months ago by @pumaiws
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IWSYEAR:2020
2IWSABT:ls3IWSAUTHOR:EjazFahadIWSAUTHOR:NowakWolfgangIWSMONTH:05IWSSUBTYPE:inproceedingsIWSTYPEDE:KonferenzbeiträgeIWSTYPEEN:contribution_to_edited_volumesIWSTYPENR:3IWSYEAR:2020
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2A composite metric for evaluating system resilience with non-idealistic performance curves

2An estimation method for real-time failure probability of a cascade dam group utilising monitoring data and ordered entropy

2Reliability sensitivity analysis with multiple failure domains based on an extended two-stage Markov chain Monte Carlo simulation

2Bayesian Failure Localization Identifies Inconsistencies between Water Distribution Network Models and Real-World Conditions

2Efficient Inference for Non-Deterministic Fractures

3Inferring Underwater Topography with Finite Volume Neural Networks

2A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

2HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time

2Degradation of bacteria for water purification in a TiO2-coated photocatalytic reactor illuminated by solar light

2Immobilization of per- and polyfluoroalkyl substances (PFAS) : experimental and model-based analysis of leaching behavior

2Evaluation of Thiobacillus denitrificans' sustainability in nitrate-reducing Fe(II) oxidation and the potential significance of Fe(II) as a growth-supporting reductant

2Large Temperatures in Water Distribution Pipes as a Water Quality Threat: Measurements and Modelling

2Bayesian3 Active Learning for Regularized Multi-Resolution Arbitrary Polynomial Chaos using Information Theory

2Development of a Combined Machine Learning and Physics-based Approach to Reduce Hydrologic Model Spin-up Time

2Surrogate-assisted Bayesian inference with ERT data for contaminant transport modelling in the subsurface

2Modeling the Ammonium Removal Processes in Household Sand Filters

2Which level of model complexity is justified by your data? A Bayesian answer

6Let’s move to spheres! Why a spherical coordinate system is rewarding when analyzing particle increment statistics

3Uncertainty quantification for a hydro-morphodynamic model of river Rhine

2Lumped hydrological model for reasonable, long- term predictions of groundwater storage and depletion

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