Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations

Abstract

Abstract 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 machine learning emulators can learn the spatiotemporal evolution of electromagnetic fields and lower-order moments of the ion velocity distribution function in near-Earth space from four 5D Vlasiator runs, each driven by steady solar wind conditions. The upstream ion number density is varied between runs at fixed grid spacing, scanning the ratio of ion inertial length to grid size. Using a graph neural network (GNN) operating on the 2D spatial simulation grid comprising 670k cells, we demonstrate that both a deterministic forecasting model (Graph-FM) and a probabilistic ensemble forecasting model (Graph-EFM) based on a latent variable formulation produce accurate predictions of future plasma states. A divergence penalty is incorporated to encourage divergence-freeness in the magnetic fields. For the probabilistic model, a continuous ranked probability score objective is added to improve the calibration of the ensemble forecasts. In terms of wall time per output step, the trained emulators run over two orders of magnitude faster on a single GPU than the Vlasiator simulations on 100 CPUs, although this comparison involves different hardware and the emulators predict only moments of the ion velocity distribution function rather than the full distribution. Most forecasted fields have Pearson correlations above 0.95 at 50 seconds lead time. Fields that exhibit degenerate (near-zero) distributions in the 5D setting are more challenging for the emulator to keep well correlated. The ensemble forecasts remain underdispersive, with spread–skill ratios of approximately 0.2–0.3, and thus provide spatially structured relative uncertainty estimates. Overall, GNNs offer a viable framework for rapid ensemble generation in hybrid-Vlasov modeling. We diagnose where such moment-based emulation succeeds and fails, and release the dataset and code as an open benchmark for the community.