5 papers
Learning Greens Operators through Hierarchical Neural Networks Inspired by the Fast Multipole Method
Emilio McAllister Fognini, Marta M. Betcke, Ben T. Cox
The Fast Multipole Method (FMM) is an efficient numerical algorithm for computation of long-ranged forces in -body problems within gravitational and electrostatic fields. This m…
Generative imaging for radio interferometry with fast uncertainty quantification
Matthijs Mars, TobÃas I. Liaudat, Jessica J. Whitney +2
With the rise of large radio interferometric telescopes, particularly the SKA, there is a growing demand for computationally efficient image reconstruction techniques. Existing rec…
Learned radio interferometric imaging for varying visibility coverage
Matthijs Mars, Marta M. Betcke, Jason D. McEwen
With the next generation of interferometric telescopes, such as the Square Kilometre Array (SKA), the need for highly computationally efficient reconstruction techniques is particu…
Parallel-in-Time Solutions with Random Projection Neural Networks
Marta M. Betcke, Lisa Maria Kreusser, Davide Murari
This paper considers one of the fundamental parallel-in-time methods for the solution of ordinary differential equations, Parareal, and extends it by adopting a neural network as a…
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
TobÃas I. Liaudat, Matthijs Mars, Matthew A. Price +3
Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensi…