相关论文: Surface Flux Transport Modeling using Physics Info…
The multi-scale structure of the solar surface magnetic field is essential for understanding both the Sun's internal dynamo processes and its external magnetic activity. The surface flux transport (SFT) model has been successful in…
The concept of surface-flux transport (SFT) is commonly used in evolving models of the large-scale solar surface magnetic field. These photospheric models are used to determine the large-scale structure of the overlying coronal magnetic…
Maps of the magnetic field at the Sun's surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Sun's Earth-facing side. One commonly used approach to mitigate…
Accurately modeling the solar magnetic field is important for understanding long-term solar activity and space weather, but it is challenging due to limited observations, especially near the poles. The Surface Flux Transport (SFT) model…
Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun-Earth line, leaving the far-side of the Sun unobserved. Surface…
Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have…
Knowledge of the global magnetic field distribution and its evolution on the Sun's surface is crucial for modeling the coronal magnetic field, understanding solar wind dynamics, computing the heliospheric open flux distribution and…
The solar dynamo relies on the regeneration of the poloidal magnetic field through processes strongly modulated by nonlinear feedbacks such as tilt quenching (TQ) and latitude quenching (LQ). These mechanisms play a decisive role in…
After emerging to the solar surface, the Sun's magnetic field displays a complex and intricate evolution. The evolution of the surface field is important for several reasons. One is that the surface field, and its dynamics, sets the…
Observations the Sun's photospheric magnetic field are often confined to the Sun-Earth line. Surface flux transport (SFT) models, such as the Advective Flux Transport (AFT) model, simulate the evolution of the photospheric magnetic field to…
We review the surface flux transport model for the evolution of magnetic flux patterns on the Sun's surface. Our underlying motivation is to understand the model's prediction of the polar field (or axial dipole) strength at the end of the…
The evolution of solar surface magnetic fields is essential for understanding solar activity and the underlying dynamic process. The surface flux transport (SFT) model is a widely used and effective tool for simulating this evolution.…
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task. It is further exacerbated by the limited availability of data, leading to poor generalizations in standard neural…
Context. Solar dipole captures important information about the large-scale solar magnetic field. The evolution of the solar magnetic field including the solar dipole can be simulated with a surface flux transport (SFT) model, but these…
With multiple vantage points around the Sun, STEREO and SDO imaging observations provide a unique opportunity to view the solar surface continuously. We use He II 304 A data from these observatories to isolate and track ten active regions…
Paleoclimatic measurements serve to understand Earth System processes and evaluate climate model performances. However, their spatial coverage is generally sparse and unevenly distributed across the globe. Statistical interpolation methods…
This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of chemical interactions and product formation in…
We harness the physics-informed neural network (PINN) approach to extend the utility of phenomenological models for particle migration in shear flow. Specifically, we propose to constrain the neural network training via a model for the…
The Sun's polar magnetic fields are directly related to solar cycle variability. The strength of the polar fields at the start (minimum) of a cycle determine the subsequent amplitude of that cycle. In addition, the polar field reversals at…
The choice of free parameters in surface flux transport (SFT) models describing the evolution of the large-scale poloidal magnetic field of the Sun is critical for the correct reproduction of the polar magnetic flux built up during a solar…