6 citations · 7 across the 4 of their papers we have counts for
5 papers
Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations
Daniel Holmberg, Ivan Zaitsev, Markku Alho +5
Hybrid-Vlasov simulations resolve ion-kinetic effects in the solar wind-magnetosphere interaction, but even 5D (2D + 3V) configurations are computationally expensive. We show that…
Graph-based Neural Space Weather Forecasting
Daniel Holmberg, Ivan Zaitsev, Markku Alho +5
Accurate space weather forecasting is crucial for protecting our increasingly digital infrastructure. Hybrid-Vlasov models, like Vlasiator, offer physical realism beyond that of cu…
SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections
Robert Jarolim, Martin Sanner, Chia-Man Hung +8
Coronagraphic observations enable direct monitoring of coronal mass ejections (CMEs) through scattered light from free electrons, but determining the 3D plasma distribution from 2D…
Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications
Ioanna Bouri, Fanni Franssila, Markku Alho +4
Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. One such application is magnetic reconne…
ML framework for global river flood predictions based on the Caravan dataset
Ioanna Bouri, Manu Lahariya, Omer Nivron +4
Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river fl…