6 papers
Decadal wave reconstruction in the Mediterranean Sea with graph neural networks
Federica Benassi, Lorenzo Mentaschi, Salvatore Causio +3
Accurate simulation and prediction of ocean waves are essential for coastal risk management and climate studies. Deep learning has shown promising results for wave modeling, but mo…
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for oc…
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…
Accurate Mediterranean Sea forecasting via graph-based deep learning
Daniel Holmberg, Emanuela Clementi, Italo Epicoco +1
Accurate ocean forecasting systems are essential for understanding marine dynamics, which play a crucial role in sectors such as shipping, aquaculture, environmental monitoring, an…
Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks
Daniel Holmberg, Manu Airaksinen, Viviana Marchi +5
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or '…