Physics Monthly -- News from RadioNets

Monday 15 Dec 2025, 14:00 → 16:00 Europe/Berlin

Zoom (Online)

Exchange of Topics, Use-Cases and Projects at the Intersection of Physics and Machine Learning

 

About RadioNets

RadioNets is a deep-learning framework for the simulation and analysis of radio interferometric data in Python. The goal is to reconstruct calibrated observations with convolutional Neural Networks to create high-resolution images. For further information, please have a look at their paper and gitHub-project.

 

 

 

 

Zoom-Link

1 Welcome and Introduction

Speaker: Dr Jens Buß (Lamarr Institute, TU Dortmund University)

2 Radio Interferometer Simulations with Pyvisgen

Pyvisgen is a Python framework, built with PyTorch to leverage GPU acceleration, that simulates measurements from radio interferometers. The framework is based on the Radio Interferometer Measurement Equation (RIME) as a central component of the simulation process. This talk will introduce the framework and recent additions of new components that improve the simulation.

Speaker: Anno Knierim

pyvisgen_physics_monthly_aknierim.pdf

3 Deep Learning-based Imaging of Multi-source Radio Skies

Speaker: Kevin Schmitz

lamarr_updates_15122025.pdf