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.
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...
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.
The paper presents a physics-informed neural network (PINN) that accelerates multilayer spectral inversion (MLSI) of solar chromospheric lines Hα 6562.8 Å and Ca II 8542.1 Å. The PINN predicts MLSI parameters from observed line profiles and uses a differentiable forward model to synthesize spectra, trained in two stages—first with spectral reconstruction loss, then fine‑tuned with conventional MLSI results on a single reference image. Applied to Fast Imaging Solar Spectrograph data, the method reproduces key spatial structures and achieves a 12–60× speedup, processing a raster in 5–15 s versus 3–5 min for traditional MLSI.
arXiv:2606. 18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity.
arXiv:2608.22782v1 Announce Type: cross Abstract: The Solar wind is a continuous flow of charged particles emanating from the solar surface and governed by complex, interacting magnetohydrodynamic pr...
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:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
arXiv:2606. 13868v1 Announce Type: cross Abstract: We present an end-to-end pipeline for estimating stellar parameters from Sloan Digital Sky Survey Data Release 12 spectra using a fully connected multitask neural network with residual blocks, whose hyperparameters are tuned via Bayesian optimization.
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.
The paper introduces Neptune, a method that uses independent coordinate neural networks to infer parameter fields in multi-physics PDEs from sparse measurements. Neptune can accurately estimate parameters with nonlinear, spatiotemporal variations, outperforming existing techniques by reducing estimation errors by up to two orders of magnitude and improving dynamic response predictions by a factor of ten. It also demonstrates strong physical extrapolation, enabling reliable predictions beyond the training data.
arXiv:2511. 04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization.
arXiv:2506. 19340v4 Announce Type: replace-cross Abstract: Whole-atmosphere models such as WACCM-X resolve coupling from the Earth surface to the Mesosphere-Lower-Thermosphere (MLT), and Ionosphere-Thermosphere (IT) systems with expensive computational costs.