arXiv:2609.37609v1 Announce Type: new Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics...
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
arXiv:2601. 12614v4 Announce Type: replace-cross Abstract: Hybrid-Vlasov simulations resolve ion-kinetic effects in the solar wind-magnetosphere interaction, but even 5D (2D + 3V) configurations are computationally expensive.
arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.
PISCES is a physics‑informed convolutional autoencoder designed to detect solar‑wind transients for space‑weather early warning. Trained on OMNI solar‑wind data without catalog labels, its loss incorporates magnetic field consistency, temperature‑velocity relations, the Parker spiral angle, and temporal smoothness penalties. During inference, PISCES decomposes the anomaly score into magnetic, plasma, physics‑relation, and residual components, enabling alarms that can precede observed sudden commencements and positive sudden impulses.
arXiv:2606. 00937v1 Announce Type: new Abstract: Neural operators provide fast surrogate models for PDE simulations, but standard architectures often treat geometry and discretization as secondary to field data.
The paper presents an energy‑based reduced‑order model for micromagnetic magnetization dynamics that couples a convolutional autoencoder with a latent neural ODE. The latent dynamics are driven by a learned scalar potential via an antisymmetric operator and a symmetric dissipative operator, ensuring monotonic energy decrease while allowing motion along level sets. The model is trained solely on short trajectory windows without explicit physical labels, and shows that antisymmetric‑dissipative and deep‑quadratic energy formulations yield superior long‑term trajectory predictions compared to purely dissipative models.
arXiv:2606. 10686v1 Announce Type: cross Abstract: The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach and treating the separatrix and equatorial current sheet as infinitesimally thin discontinuities.
The paper introduces equation recast, a method that transforms parametric operator learning into learning a single canonical operator. By analytically deriving parameter-induced variations from the governing equations and incorporating them as effective sources, the approach enables zero‑shot predictions across new parameter regimes and supports extrapolation in multi‑parameter, nonlinear, and singular PDE settings. It also integrates sparse heterogeneous datasets, uses loss of convergence as an internal warning, and demonstrates unification of electron‑temperature data from multiple tokamak geometries in high‑fidelity nuclear fusion simulations.
arXiv:2609.24882v1 Announce Type: new Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hi...
arXiv:2609.35884v1 Announce Type: new Abstract: Divergence-free vector fields are fundamental state variables in incompressible flows and many PDE systems. Redundant parameterizations, including Neur...
arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...