32 citations · 104 across the 10 of their papers we have counts for
5 papers · 1 filter
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…