Magnetohydrodynamics

Latest papers

11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

PHASE: Multi-Regime Modeling of Incompressible Magnetohydrodynamics
Sep 29, 2026Radhika Achikanath Chirakkara, Rajdeep Haldar, Zezheng Song +1Magnetohydrodynamics PDE Operator Learning
Canadian Institute for Theoretical Astrophysics, University of Toronto, ON, Canada, M5S 3H8 · Department of Statistics, Purdue University, IN, United States of America, 47907 · University of Maryland, College Park, MD, United States of America, 20742 +1
Real-Time Monitoring of MHD Liquid Metal Flows with Shallow Recurrent Decoders
Aug 28, 2026Claudio Scardino, Stefano Riva, Carolina Introini +4Magnetohydrodynamics Reduced-Order Modeling
Energy Department, CeSNEF - Nuclear Engineering Division, Politecnico di Milano, Milano · Autodesk Research, 6 Agar Street, London, WC2R 0QE, UK · Argonne National Laboratory, Lemont, IL, 60439, United States of America +1
Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State
Aug 24, 2026Vignesh Kumar Pandian Sathia, Reza Mansouri, Dustin J. Kempton +2Magnetohydrodynamics Computational Fluid Dynamics
Department of Computer Science, Georgia State University Atlanta, USA · Predictive Sciences Inc. San Diego, USA
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
Jun 28, 2026Alexander ScheinkerUncertainty Quantification Autoregressive Diffusion
Los Alamos National Laboratory, Los Alamos, NM, 87545.
Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator
Jun 13, 2026Jay Phil Yoo, William Howes, Yashika Ghai +3Transfer Learning PDE Operator Learning
Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering Department, University of Illinois Urbana-Champaign, Urbana, IL, USA · Fusion Energy Division, Oak Ridge National Lab, Oak Ridge, TN, USA · National Center for Supercomputing Applications, Urbana, IL, USA +2
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs
May 31, 2026Lennon J. Shikhman, Shane GilbertiePDE Surrogate Modeling Scientific ML
College of Computing, Georgia Institute of Technology, Atlanta, 30332, GA, USA · Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, Melbourne, 32901, FL, USA
Learning Neural Operator Surrogates for the Black Hole Accretion Code
Apr 28, 2026Matthias Nägele, Cedric Bös, Chester Tan +3Computational Fluid Dynamics Transformer Neural Operator
Institute for Theoretical Physics and Astrophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany · Chair for Machine Learning for Complex Networks, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
Apr 2, 2026M. Lo Verso, C. Introini, E. Cervi +3Magnetohydrodynamics Computational Fluid Dynamics
Department of Energy, Politecnico di Milano, Milano, 20133, Italy · MATHEP Group, Dept. of Energy "Galileo Ferraris", Politecnico di Torino, Torino, Italy · Autodesk Research, 6 Agar Street, London UK +1
Segmenting Superbubbles in a Simulated Multiphase Interstellar Medium using Computer Vision
Mar 29, 2026Jing-Wen Chen, Alex S. Hill, Anna Ordog +2Magnetohydrodynamics 3D Instance Segmentation
Department of Computer Science, Math, Physics & Statistics, University of British Columbia, Okanagan Campus 1177 Research Rd, Kelowna, BC V1V 1V7, Canada · Dominion Radio Astrophysical Observatory, Herzberg Research Centre for Astronomy and Astrophysics, National Research Council Canada, PO Box 248, Penticton, BC V2A 6J9, Canada · Department of Physics & Astronomy, University of Western Ontario, 1151 Richmond Street, London, ON, N6A 3K7, Canada +1
Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics
Mar 11, 2026M. Lo Verso, C. Introini, E. Cervi +3Magnetohydrodynamics Neural Surrogate Modeling
Department of Energy, Politecnico di Milano, Milano, 20133, Italy · MATHEP Group, Dept. of Energy "Galileo Ferraris", Politecnico di Torino, Torino, Italy · Autodesk Research, 6 Agar Street, London UK +1
Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations
Mar 1, 2026Yunlong Li, Fei Wang, Lingxiao LiStructure-Preserving Neural Networks Magnetohydrodynamics
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China. · School of Mathematics and Statistics & State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China. · School of Mathematics and Statistics, Henan University, Kaifeng, Henan 475004, China. +1

Sep 29, 2026J/K move · Enter open · S save

PHASE: Multi-Regime Modeling of Incompressible Magnetohydrodynamics

Radhika Achikanath Chirakkara, Rajdeep Haldar, Zezheng Song, Jiequn Han

Canadian Institute for Theoretical Astrophysics, University of Toronto, ON, Canada, M5S 3H8 · Department of Statistics, Purdue University, IN, United States of America, 47907 · University of Maryland, College Park, MD, United States of America, 20742+1

Magnetohydrodynamics PDE Operator Learning Physics-Informed Neural Operators Computational Fluid Dynamics

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Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive. Machine-learning surrogates enable fast inference by learning reusable solution operators, yet existing models require separate training for each physical regime, limiting generalization across varying parameter settings. We introduce PHASE, a PHysics-Adaptive Scalable operator with residual Error correction, designed to model incompressible MHD across varying physical parameters with a single model. PHASE combines transfer learning, regime-aware adaptation, physics-centered learning, and residual refinement to improve both physical fidelity and generalization across MHD regimes. Together, these improvements achieve state-of-the-art prediction accuracy on two-dimensional MHD turbulence by reducing relative

L_2

errors on physical fields by more than an order of magnitude compared to prior MHD neural-operator baselines. Moreover, PHASE generalizes successfully to unseen parameter values without retraining, demonstrating the cross-regime adaptability expected from operator learning. We evaluate PHASE beyond point-wise prediction errors using derived physical fields, spectral analysis, and distribution statistics, consistently observing improved physical fidelity. We further show that our framework can accurately simulate MHD instabilities by testing it on the Kelvin--Helmholtz instability, demonstrating the robustness of our method.