Water is unique – no other element is so ubiquitous, vital, vulnerable and threatening at the same time. We must secure our access to clean water, shield our civilization from droughts and floods, use water sustainably in food and energy production, and protect water as part of our environment. Our department has the mission to help master these challenges.
Simulation technology provides the ability to see through walls and obstacles in past, present and future. Safe engineering requires conclusive and reliable recommendations, but the models and data used in simulation are always imperfect.
For handling the resulting uncertainties and risks, we develop concepts, models and tools for stochastic simulation. Our focus is on natural subsurface resources (e.g., groundwater, drinking water and carbon reservoirs) and on energy-related systems (e.g., carbon dioxide storage, geothermal energy, energy system planning, battery safety).
Our department has strong collaborations with internationally renowned researchers. These collaborations comprise projects related to environmental modelling, underground contaminant transport, statistics, optimization, quantification of uncertainty, system reliability, system security and energy systems. Our PhD-students typically perform a research stay at our partners during their projects.
Current Research Projects
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Bayesian, Causal, Universal Differential Equation Learner
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CA20136 - Opportunistic Precipitation Sensing Network (OPENSENSE)
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Developing Algorithms and Software Tools for Performing Parallel Simulation of Dynamic Processes
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Estimating Groundwater Levels at Multiple Scales by Fusing Data, Geostatistics, and Physics
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Identifying Climate Change Driven Critical Weather Conditions that Result in Dramatic Shifts in Lakes
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Improving the Predictive Quality of Repository-Relevant Simulations Through Optimal Data Acquisition and Smart Monitoring
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Influence of Soil Temperature on the Warming of Drinking Water in Water Distribution Pipe Networks – Development of a Soil Model
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Learning Mechanisms of Phenomena from Observations
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Machine Learning for Planning Water Supply Infrastructure in the Face of Climate Change
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Methods for Coordinated Optimization of Water Supply Systems in Future Energy Systems under Deep Climatic Uncertainty
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Physics Machine Learning for Spatio-Temporal Systems: ANN Generalization using Adaptive Grids, Domain Decomposition and Parallelization
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Random Fracture Models - Towards Statistical Realism and Validation
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Surrogate Modeling Coupled With Bayesian Active Learning Evaluated Using Information Theory Metrics for Computationally Expensive Sediment Transport Models
Past Research Projects
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A Hybrid Stochastic-Deterministic Model Calibration Method for CO2 Storage in Geological Formations
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Bayesian Legitimacy of Hydrogeological Model Concepts
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COMPUtational Framework for Modern Calibration and Validation of Mathematical Models of Subsurface Flows (COMPUFLOW)
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Catchments as Reactors: Metabolism of Pollutants on the Catchment Scale
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Data Assimilation for Energy Storage to Improve Operational Control
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Early-Warning Monitoring Systems for Drinking Water Resource Protection
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Efficient Concepts in Optimal Design of Experiments
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Integrative Approach for Conditioning, Robust Design and Control in the Subsurface
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Intelligent Measurement of the Aquifer: Closing the Groundwater Balance for Sustainable Management in the Face of Unregulated Groundwater Extraction
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Joint Data Compression and Model Reduction for Conditional Stochastic Modeling of Subsurface Flow and Transport Processes
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Markov Chain Monte Carlo Methods for Bayesian Inversion of Groundwater Flow in Porous Media
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Modeling Strategies for Gas Migration in Porous Media
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Optimal Exploration and Delineation for Drinking Water Well Protection under Transient Flow Conditions
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Overcoming the Mistaken Overconfidence in Parameter Estimation for Soil Moisture Models
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Parameter Inference for Nonlinear Modeling of Flow in Geological Formations with Application to CO2 Storage: Development of an Adaptive Bayesian Arbitrary Multi-Resolution Polynomial Chaos Expansion
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Physics-Informed ANNs for Dynamic, Distributed and Stochastic Systems (SmartANN)
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Prioritizing Major Predictive Uncertainty Sources in Coupled Hydrosystem Models
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Probabilistic Identification of Water Pipe Network Failure
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Probabilistic Risk Assessment for CO2 Storage Scenarios via Massive Stochastic Model Reduction
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Probabilistic Risk-Management as Integral Drinking Water Concept
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Robust Expansion Planning of Hydropower and Energy Storage Systems
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Statistical Dependence-Structure and Process-Memory Analysis of non-Fickian Transport processes in Porous Media
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The Architecture of Damage Nucleation: Multivariate Phenomenological Description, Identification, and Simulation
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Thermal Runaway of Lithium Batteries