32 citations · 104 across the 10 of their papers we have counts for
10 papers
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Tim Schneider, Amin Totounferoush, Wolfgang Nowak +1
Symbolic Regression (SR) allows for the discovery of scientific equations from data. To limit the large search space of possible equations, prior knowledge has been expressed in te…
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin +1
Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy…
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte +3
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the…
Investigating the Pilot Point Ensemble Kalman Filter for geostatistical inversion and data assimilation
Johannes Keller, Harrie-Jan Hendricks Franssen, Wolfgang Nowak
Parameter estimation has a high importance in the geosciences. The ensemble Kalman filter (EnKF) allows parameter estimation for large, time-dependent systems. For large systems, t…
Diagnosis of model-structural errors with a sliding time-window Bayesian analysis
Han-Fang Hsueh, Anneli Guthke, Thomas Wöhling +1
Deterministic hydrological models with uncertain, but inferred-to-be-time-invariant parameters typically show time-dependent model structural errors. Such errors can occur if a hyd…
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Timothy Praditia, Matthias Karlbauer, Sebastian Otte +3
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task. It is further exacerbated by the limited availability of…
Sequential pCN-MCMC, an efficient MCMC method for Bayesian inversion of high-dimensional multi-Gaussian priors
Sebastian Reuschen, Fabian Jobst, Wolfgang Nowak
In geostatistics, Gaussian random fields are often used to model heterogeneities of soil or subsurface parameters. To give spatial approximations of these random fields, they are d…
Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation
Stefania Scheurer, Aline Schäfer Rodrigues Silva, Farid Mohammadi +4
Geochemical processes in subsurface reservoirs affected by microbial activity change the material properties of porous media. This is a complex biogeochemical process in subsurface…
Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations
Christoph Dibak, Wolfgang Nowak, Frank Dürr +1
Numerical simulations on mobile devices are an important tool for engineers and decision makers in the field. However, providing simulation results on mobile devices is challenging…
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario
Markus Köppel, Fabian Franzelin, Ilja Kröker +8
A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough compariso…