Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State

• URL: http://arxiv.org/abs/2608.22782v1
• Date: Mon, 24 Aug 2026 04:11:59 GMT
• Title: Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State
• Abstract summary: We study the problem of learning the multi-field multi-scale solar magnetohydrodynamic state at 30 solar radii.<n>We employ a Local Neural Operator (LocalNO) that learns mappings between input and output function spaces.<n>The resulting predictions along with inputs are intended to serve as boundary condition variables for future inner-heliospheric modeling pipelines.
• Score: 1.2314765641075438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Solar wind is a continuous flow of charged particles emanating from the solar surface and governed by complex, interacting magnetohydrodynamic processes. Accurate specification of inner-boundary conditions is essential for heliospheric modeling and solar-wind prediction. In many practical applications, only a subset of interacting multi-field variables is directly available, but for a comprehensive view of solar wind prediction and downstream magnetohydrodynamic simulations, a more complete boundary state is required. In this work, we study the problem of learning the multi-field multi-scale solar magnetohydrodynamic state at 30 solar radii ($R_\odot$) using operator learning. Specifically, given the radial velocity and radial magnetic field, we aim to reconstruct the non-radial velocity and magnetic field components, radial and non-radial current density, thermodynamic density, and pressure components. This mapping is highly nonlinear, spatially coupled, and multi-scale, making it a challenging task for data-driven scientific machine learning. To address this problem, we employ a Local Neural Operator (LocalNO) that learns mappings between input and output function spaces while retaining locality and resolution-awareness. Unlike conventional regression models and autoencoder models, neural operators are better suited for learning structured field-to-field transformations arising from physical systems. The resulting predictions along with inputs are intended to serve as boundary condition variables for future inner-heliospheric modeling pipelines.

Related papers

• From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
  We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
  arXiv  Detail & Related papers  (2025-12-01T11:47:49Z)
• Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator [0.0]
  We develop a surrogate for steady state solar wind modeling, using a Spherical Fourier Neural Operator (SFNO)<n>We show that the SFNO achieves comparable or better performance across several metrics.<n>As a flexible and trainable approach, SFNO enables efficient real-time forecasting and can improve with more data.
  arXiv  Detail & Related papers  (2025-11-27T05:05:29Z)
• Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator [0.0]
  We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity.<n>The model improves accuracy in distant regions compared to a single-step approach.<n>Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance.
  arXiv  Detail & Related papers  (2025-11-25T20:30:03Z)
• Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) [51.510040541600176]
  We propose a novel approach to modeling the rotation of moving objects in computer vision.<n>Our approach is agnostic to energy and momentum conservation while being robust to input noise.<n>By learning to approximate object dynamics from noisy states during training, our model attains robust extrapolation capabilities in simulation and various real-world settings.
  arXiv  Detail & Related papers  (2025-08-11T09:03:10Z)
• Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport [41.94295877935867]
  Local-Global Convolutional Neural Network (LGCNN) approach is introduced.<n>Model is first systematically analyzed based on random input fields.<n>Then, the model is trained on a handful of cut-outs from a real-world Classical map of the Munich region in Germany.
  arXiv  Detail & Related papers  (2025-07-08T15:06:15Z)
• Interaction Field Matching: Overcoming Limitations of Electrostatic Models [85.87349969357068]
  Electrostatic field matching (EFM) has recently appeared as a physics-inspired paradigm for data generation and transfer using the idea of an electric capacitor.<n>We propose Interaction Field Matching (IFM), a generalization of EFM which allows using general interaction fields beyond the electrostatic one.<n>We show the performance on a series of toy and image data transfer problems.
  arXiv  Detail & Related papers  (2025-06-03T14:45:14Z)
• RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction [76.24833675757033]
  We propose a physics-informed generative learning approach, named RadioDiff-$k2$, for accurate and efficient multipath-aware radio map (RM) construction.<n>We show that the proposed RadioDiff-$k2$ framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks.
  arXiv  Detail & Related papers  (2025-04-22T06:28:13Z)
• Surface Flux Transport Modeling using Physics Informed Neural Networks [0.0]
  Surface Flux Transport (SFT) modeling helps us to simulate and analyse the transport and evolution of magnetic flux on the solar surface. In this work, we demonstrate the use of machine learning techniques in solving magnetic flux transport, making it accurate. We have developed a novel Physics-Informed Neural Networks (PINN)-based model to study the evolution of Bipolar Magnetic Regions (BMRs) using SFT.
  arXiv  Detail & Related papers  (2024-09-03T09:41:07Z)
• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media [1.5791732557395552]
  We describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses.
  arXiv  Detail & Related papers  (2021-04-30T02:14:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.