2 citations · 2 across the 6 of their papers we have counts for
7 papers
Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
Francesco Pio Ramunno, Simone Mestici, Igino Coco +5
Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt radio communications, and threaten power infrastructure, making accurate forecasting o…
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
Rudy Morel, Francesco Pio Ramunno, Jeff Shen +18
Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather predicti…
Enhancing Image Resolution of Solar Magnetograms: A Latent Diffusion Model Approach
Francesco Pio Ramunno, Paolo Massa, Vitaliy Kinakh +3
The spatial properties of the solar magnetic field are crucial to decoding the physical processes in the solar interior and their interplanetary effects. However, observations from…
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept
Grégoire Francisco, Francesco Pio Ramunno, Manolis K. Georgoulis +3
The solar magnetized corona is responsible for various manifestations with a space weather impact, such as flares, coronal mass ejections (CMEs) and, naturally, the solar wind. Mod…
A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX
Paolo Massa, Simon Felix, László István Etesi +7
The Spectrometer/Telescope for Imaging X-rays (STIX) on-board the ESA Solar Orbiter mission retrieves the coordinates of solar flare locations by means of a specific sub-collimator…
Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution
Francesco Pio Ramunno, Hyun-Jin Jeong, Stefan Hackstein +3
Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduc…
Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data
Francesco P. Ramunno, S. Hackstein, V. Kinakh +4
Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient…