12 papers
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…
LEMUR: Learned Multi-Vector Retrieval
Elias Jääsaari, Ville Hyvönen, Teemu Roos
Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vector representations in information retri…
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…
Breakable Machine: A K-12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)
Olli Hilke, Nicolas Pope, Juho Kahila +4
This paper, submitted to the special track on resources for teaching AI in K-12, presents an eXplainable AI (XAI)-based classroom game "Breakable Machine" for teaching critical, tr…
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…