35 citations · 74 across the 9 of their papers we have counts for
9 papers
Improving physics-informed DeepONets with hard constraints
Rüdiger Brecht, Dmytro R. Popovych, Alex Bihlo +1
Current physics-informed (standard or deep operator) neural networks still rely on accurately learning the initial and/or boundary conditions of the system of differential equation…
Towards replacing precipitation ensemble predictions systems using machine learning
Rüdiger Brecht, Alex Bihlo
Precipitation forecasts are less accurate compared to other meteorological fields because several key processes affecting precipitation distribution and intensity occur below the r…
M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts
Rüdiger Brecht, Alex Bihlo
In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), wh…
Model-agnostic machine learning of conservation laws from data
Shivam Arora, Alex Bihlo, Rüdiger Brecht +1
We present a machine learning based method for learning first integrals of systems of ordinary differential equations from given trajectory data. The method is model-agnostic in th…
Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks
Rüdiger Brecht, Alex Bihlo
Ensemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic con…
Selective decay for the rotating shallow-water equations with a structure-preserving discretization
Rüdiger Brecht, Werner Bauer, Alexander Bihlo +2
Numerical models of weather and climate critically depend on long-term stability of integrators for systems of hyperbolic conservation laws. While such stability is often obtained…
Rotating shallow water flow under location uncertainty with a structure-preserving discretization
Rüdiger Brecht, Long Li, Werner Bauer +1
We introduce a physically relevant stochastic representation of the rotating shallow water equations. The derivation relies mainly on a stochastic transport principle and on a deco…
Variational integrator for the rotating shallow-water equations on the sphere
Rüdiger Brecht, Werner Bauer, Alexander Bihlo +2
We develop a variational integrator for the shallow-water equations on a rotating sphere. The variational integrator is built around a discretization of the continuous Euler-Poinca…
A well-balanced meshless tsunami propagation and inundation model
Rüdiger Brecht, Alexander Bihlo, Scott MacLachlan +1
We present a novel meshless tsunami propagation and inundation model. We discretize the nonlinear shallow-water equations using a well-balanced scheme relying on radial basis funct…