1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2023
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…
physics.ao-ph2023★ 1 cited
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…
physics.ao-ph2023★ 1 cited
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…