35 citations · 74 across the 9 of their papers we have counts for
4 papers · 1 filter
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…