Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere

• URL: http://arxiv.org/abs/2512.16175v1
• Date: Thu, 18 Dec 2025 04:49:20 GMT
• Title: Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
• Abstract summary: We develop a data-driven model of the Martian induced magnetospheric magnetic field using Physics-Informed Neural Network (PINN)<n>We identify key dependencies of magnetic field configuration on solar wind parameters, including the hemispheric asymmetries of the draped field line strength in the Mars-Solar-Electric coordinates.
• Score: 8.601043065702704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 been exclusively physics-based, relying on computationally intensive simulations. For the first time, we develop a data-driven model of the Martian induced magnetospheric magnetic field using Physics-Informed Neural Network (PINN) combined with MAVEN observations and physical laws. Trained under varying solar wind conditions, including B_IMF, P_SW, and θ_cone, the data-driven model accurately reconstructs the three-dimensional magnetic field configuration and its variability in response to upstream solar wind drivers. Based on the PINN results, we identify key dependencies of magnetic field configuration on solar wind parameters, including the hemispheric asymmetries of the draped field line strength in the Mars-Solar-Electric coordinates. These findings demonstrate the capability of PINNs to reconstruct complex magnetic field structures in the Martian induced magnetosphere, thereby offering a promising tool for advancing studies of solar wind-Mars interactions.

Related papers

• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena [0.0]
  Major space weather contributors are the solar wind and coronal mass ejections.<n>We adapt a foundation model for solar physics to create embeddings suitable for solar wind structure analysis.<n>As a first proof-of-concept, it lays the groundwork for future improvements toward more reliable space weather predictions.
  arXiv  Detail & Related papers  (2025-11-13T00:56:00Z)
• 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)
• Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data [77.40388962445168]
  This study explores the feasibility of deriving Mercury's regolith elemental composition from in-situ measurements of its neutral exosphere using deep neural networks (DNNs)<n>We present a supervised feed-forward DNN architecture that predicts the chemical elements of the surface regolith below.<n>It serves as an estimator for the surface-exosphere interaction and the processes leading to exosphere formation.
  arXiv  Detail & Related papers  (2025-05-16T09:52:45Z)
• Controllable and Continuous Quantum Phase Transitions in Intrinsic Magnetic Topological Insulator [50.54133633499971]
  We study the intrinsic magnetic topological material MnBi2Te4 in which the heavy n-type doping features are strongly suppressed.<n>Based on angle-resolved photoemission spectroscopy, transport measurements, and first-principles calculations, we reveal two magnetism-induced TPTs.<n>Our work paves the way for the realization of intrinsic magnetic topological states in MnBi2Te4 family and provides an ideal platform for achieving controllable and continuous TPTs.
  arXiv  Detail & Related papers  (2025-03-08T03:46:54Z)
• Universal Reconstruction of Complex Magnetic Profiles with Minimum Prior Assumptions [12.951457419224631]
  We present a novel and efficient method for reconstructing spatially varying magnetization density from measured magnetic fields.<n>We validate our method by simulating diverse magnetic structures under realistic experimental conditions.<n>Our approach provides a versatile and universal tool for investigating complex magnetization profiles, paving the way for future quantum sensing experiments.
  arXiv  Detail & Related papers  (2024-11-28T03:15:54Z)
• 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)
• Magnetogram-to-Magnetogram: Generative Forecasting of Solar Evolution [0.0]
  We introduce a novel method to predict the evolution of the solar line-of-sight (LoS) magnetogram using image-to-image translation with Denoising Diffusion Probabilistic Models (DDPMs) Our approach combines "computer science metrics" for image quality and "physics metrics" for physical accuracy to evaluate model performance. The results indicate that DDPMs are effective in maintaining the structural integrity, the dynamic range of solar magnetic fields, the magnetic flux and other physical features such as the size of the active regions, surpassing traditional persistence models, also in flaring situation.
  arXiv  Detail & Related papers  (2024-07-16T12:28:10Z)
• A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance [59.21686775951903]
  Various animals exhibit accurate navigation using environment cues. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps.
  arXiv  Detail & Related papers  (2024-02-06T13:20:56Z)
• Physics-driven machine learning for the prediction of coronal mass ejections' travel times [46.58747894238344]
  Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are correlated to geomagnetic storms and may induce the generation of Solar Energetic Particles streams. The present paper introduces a physics-driven artificial intelligence approach to the prediction of CMEs travel time.
  arXiv  Detail & Related papers  (2023-05-17T08:53:29Z)
• Solar Active Region Magnetogram Image Dataset for Studies of Space Weather [0.0]
  The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. This dataset is a minimally processed, user dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research.
  arXiv  Detail & Related papers  (2023-05-16T14:44:24Z)
• Surpassing the Energy Resolution Limit with ferromagnetic torque sensors [55.41644538483948]
  We evaluate the optimal magnetic field resolution taking into account the thermomechanical noise and the mechanical detection noise at the standard quantum limit. We find that the Energy Resolution Limit (ERL), pointed out in recent literature, can be surpassed by many orders of magnitude.
  arXiv  Detail & Related papers  (2021-04-29T15:44:12Z)

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.