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...
By Radhika Achikanath Chirakkara, Rajdeep Haldar, Zezheng Song, Jiequn Han
arXiv:2609.08463v1 Announce Type: cross
Abstract: We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections. Starting from an ensemble of deep neura...
By Krzysztof M. Graczyk, Beata E. Kowal, Rwik Dharmapal Banerjee, Jose Luis Bonilla, Hemant Prasad, Jan T. Sobczyk
arXiv:2608. 04222v1 Announce Type: cross Abstract: Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly.
By Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Peyman Givi, Xiaowei Jia, Runlong Yu
arXiv:2507. 00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy.
By Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. Lermusiaux
The paper presents an inverse Physics-Informed Neural Network (PINN) framework that infers the scrape‑off layer (SOL) perpendicular heat conductivity κ_π(n,T) from radial electron density and temperature measurements combined with a reduced one‑dimensional transport equation. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles, while the third models the conductivity as a function of local density and temperature. The method is validated on synthetic data, achieving errors below 10 % in the data‑constrained region, and is then applied to experimental data from the TJ‑II stellarator using a helium‑beam diagnostic to estimate the effective SOL conductivity.
By J. Gallego (Departamento de Tecnolog\'ia, CIEMAT, Spain), P. Protopapas (Harvard John A. Paulson School of Engineering and Applied Sciences, USA), A. Bustos (Departamento de Tecnolog\'ia, CIEMAT, Spain), A. Alonso (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), S. Barquero (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), A. Baciero (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), I. Rivera (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), J. A. Mor\'i\~nigo (Departamento de Tecnolog\'ia, CIEMAT, Spain), R. Mayo-Garc\'ia (Departamento de Tecnolog\'ia, CIEMAT, Spain)
arXiv:2606. 14729v1 Announce Type: cross Abstract: Turbulent combustion simulations are crucial for many scientific and engineering systems.
By Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng