35 citations · 74 across the 9 of their papers we have counts for
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cs.LG2023★ 3 cited
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…
cs.LG2023★ 2 cited
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…
cs.LG2022★ 2 cited
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…