Neural metamodelling of fields: Towards a new deal in computational electromagnetics

In computational electromagnetism there are manyfold advantages when using machine learning methods, because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, thanks to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for the neural meta-modeling of fields, in spite of different levels of cost and accuracy. In the paper it is shown how CNNs can be trained to solve problems of optimal shape synthesis, with training data sets based on finite-element analyses of electric and magnetic fields. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. The shape design of a MEMS design and the TEAM workshop problem 35 are considered as the case studies

The IceCube Realtime Alert System

Following the detection of high-energy astrophysical neutrinos in 2013, their origin is still unknown. Aiming for the identification of an electromagnetic counterpart of a rapidly fading source, we have implemented a realtime analysis framework for the IceCube neutrino observatory. Several analyses selecting neutrinos of astrophysical origin are now operating in realtime at the detector site in Antarctica and are producing alerts to the community to enable rapid follow-up observations. The goal of these observations is to locate the astrophysical objects responsible for these neutrino signals. This paper highlights the infrastructure in place both at the South Pole detector site and at IceCube facilities in the north that have enabled this fast follow-up program to be developed. Additionally, this paper presents the first realtime analyses to be activated within this framework, highlights their sensitivities to astrophysical neutrinos and background event rates, and presents an outlook for future discoveries.Comment: 33 pages, 9 figures, Published in Astroparticle Physic