184 citations
- Helmholtz Centre for Environmental ResearchDE4 papers
- Heidelberg UniversityDE2 papers
- Monash UniversityAU2 papers
- University of StuttgartDE2 papers
- American Museum of Natural HistoryUS1 paper
- Australian National UniversityAU1 paper
- Center for Scalable Data Analytics and Artificial IntelligenceDE1 paper
- City College of New YorkUS1 paper
- German Research Centre for Artificial IntelligenceDE1 paper
- Hamburger Fern-HochschuleDE1 paper
- Kernkraftwerk GundremmingenDE1 paper
- Leiden UniversityNL1 paper
8 papers
Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang +2
The forecast accuracy of machine learning (ML) weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous…
Assessment of models for nonlinear oscillatory flow through a hexagonal sphere pack
Lukas Unglehrt, Michael Manhart
We review models for unsteady porous media flow in the volume-averaging framework and we discuss the theoretical relations between the models and the definition of the model coeffi…
Stable and locally mass- and momentum-conservative control-volume finite-element schemes for the Stokes problem
Martin Schneider, Timo Koch
We introduce new control-volume finite-element discretization schemes suitable for solving the Stokes problem. Within a common framework, we present different approaches for constr…
Contemporary climate analogs project north-south polarization of urban water-energy nexus across US cities under warming climate
Renee Obringer, Roshanak Nateghi, Jessica Knee +2
Despite the coupled nature of water and electricity demand, the two utilities are often managed by different entities with minimal interaction. Neglecting the water-energy demand n…
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
Sergey Oladyshkin, Timothy Praditia, Ilja Kröker +3
Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and…
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…