用物理约束的Transformer模型，快速校准火星探测器磁力数据。
Magnetic Field Data Calibration with Transformer Model Using Physical Constraints: A Scalable Method for Satellite Missions, Illustrated by Tianwen-1
- 基于Transformer的神经网络，融入麦克斯韦方程物理约束。
- 校准时间从数周缩短至数小时，预测仅需数秒。
- 适合行星探测与空间天气研究，可扩展至未来任务。
本文提出一种融合物理约束的新型磁力数据校准方法，应用于天问一号火星任务的磁测数据。通过引入基于麦克斯韦方程的物理先验，构建基于Transformer的神经网络模型，有效处理卫星运动、仪器干扰与环境噪声导致的测量异常。相比传统需数周甚至数月人工干预的方法，该方法可在数小时完成校准，预测耗时仅数秒。显著提升数据精度与物理一致性，为行星磁层研究、空间天气建模及未来深空探测任务中的磁数据处理提供高效、可扩展的技术框架。
原文摘要 · Abstract (English)
This study introduces a novel approach that integrates the magnetic field data correction from the Tianwen-1 Mars mission with a neural network architecture constrained by physical principles derived from Maxwell's equation equations. By employing a Transformer based model capable of efficiently handling sequential data, the method corrects measurement anomalies caused by satellite dynamics, instrument interference, and environmental noise. As a result, it significantly improves both the accuracy and the physical consistency of the calibrated data. Compared to traditional methods that require long data segments and manual intervention often taking weeks or even months to complete this new approach can finish calibration in just minutes to hours, and predictions are made within seconds. This innovation not only accelerates the process of space weather modeling and planetary magnetospheric studies but also provides a robust framework for future planetary exploration and solar wind interaction research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。