143 citations · 757 across the 54 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…
Improving physics-informed neural networks with meta-learned optimization
Alex Bihlo
We show that the error achievable using physics-informed neural networks for solving systems of differential equations can be substantially reduced when these networks are trained…
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…