Abstract Mathematics lies at the heart of engineering science and is very important for
capturing and modeling of diverse processes. These processes may be naturally-occurring …

We introduce a new class of hybrid preconditioners for solving parametric linear systems of
equations. The proposed preconditioners are constructed by hybridizing the deep operator …

Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse
data. Its quality strongly depends on the choice of known data. Optimising their spatial …

In an industrial plant it is necessary to monitor the operation of the equipment. Deviations
from normal operation should be detected as early as possible to avoid production failures …

Diffusion-based inpainting can reconstruct missing image areas with high quality from
sparse data, provided that their location and their values are well optimised. This is …

Solving variational image segmentation problems with hidden physics is often expensive
and requires different algorithms and manually tuned model parameters. The deep learning …

The smooth interaction between the pantograph and the catenary is crucial for the
operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary …

Long-term evolutions of parabolic partial differential equations, such as the heat equation,
are the subject of interest in many applications. There are several numerical solvers marking …

Quantization of convolutional neural networks (CNNs) is a common approach to ease the
computational burden involved in the deployment of CNNs, especially on low-resource edge …

This research illustrates how time-series forecasting employing recurrent neural networks
(RNNs) can be used for anomaly detection in particle accelerators—complex machines that …