物理信息神经网络在空天领域的应用与展望

doi:10.12305/j.issn.1001-506X.2026.04.16

摘要/Abstract

摘要：

航空航天领域正蓬勃发展，随着人工智能（artificial intelligence, AI）技术不断取得突破性成果，AI技术浪潮与航空航天领域的结合愈发被广泛关注。物理信息神经网络(physics-informed neural networks, PINN)作为AI领域的全新计算范式，其理论和应用创新层出不穷。由于PINN具备将科学问题中所包含的物理信息约束与训练数据样本有效融合的能力，因此尤其适用于动态环境复杂、多扰动因素鲜明的空天任务中。首先简要阐述PINN的定义和基本框架，其次在回顾PINN理论与应用研究现状的基础上，围绕PINN在空天领域的应用现状进行分类讨论与梳理，随后提出一种基于PINN的轨道外推研究方案。最后，针对PINN应用于空天领域的前景、局限性和探索路径进行总结和展望。

关键词: 人工智能, 深度学习, 物理信息神经网络, 航空航天

Abstract:

The rapid advancement of aerospace, alongside breakthroughs in artificial intelligence （AI）, has drawn increasing attention to the integration of AI with aerospace applications. Physics-informed neural networks （PINN）, as a novel computational paradigm within AI, have showcased continuous theoretical and application innovations. With their unique ability to seamlessly integrate physical constraints from scientific problems with training data samples, PINN are particularly well-suited for addressing the challenges of complex dynamics and significant perturbations inherent in aerospace tasks. This paper begins by providing a concise explanation of the definition and fundamental framework of PINN. Subsequently, it reviews the current research progress in PINN’s theory and applications, systematically categorizing and analyzing their use in aerospace. Furthermore, a PINN-based orbit propagation research scheme is proposed. Finally, the paper concludes with a comprehensive discussion of the potential, limitations, and future research directions of PINNs in aerospace.

Key words: artificial intelligence (AI), deep learning, physics-informed neural networks (PINN), aerospace

中图分类号:

TP 183

林炅, 高朋, 洪文兴. 物理信息神经网络在空天领域的应用与展望[J]. 系统工程与电子技术, 2026, 48(4): 1264-1282.

Jiong LIN, Peng GAO, Wenxing HONG. Applications and prospects of physics-informed neural networks in aerospace[J]. Systems Engineering and Electronics, 2026, 48(4): 1264-1282.

图/表 17

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表2

PINN在航空领域的现有研究总结"

应用领域	文献	研究对象	研究内容	创新或成果
航空流场重构	[66]	低雷诺数流场	稳态和瞬态层流模拟	预测值与数值解法高度一致
	[67]	高速空气动力学	欧拉方程正逆向重构	在逆向问题表现出色
	[68]	超音速流场	可压缩流动问题	利用密度梯度数据，超越传统方法
	[69]	机翼	绕机翼无黏流动问题	高效求解流动模拟和形状逆设计的参数化问题
	[70]	机翼	特定翼型的流场建模	实现升阻比的最大化设计
	[71]	高超音速飞行器	高马赫数进气道流场	多尺度感知残差提升精度与效率
气动数据与飞行动力学	[72]	飞机	气动参数辨识	含噪数据下能提高辨识精度
	[73]	NACA0012翼型	气动数据建模	将PINN与CFD仿真结果结合，克服精度不足的问题
	[74]	导弹	气动不确定性问题	利用增强输入变量的PINN和仿真时序数据，输出精确估计
	[75]	空气动力学模型	非线性空气动力效应	极端飞行条件下生成高精度数据
航空发动机工程	[76]	航发燃烧机理	内部复杂的湍流燃烧	实现稀疏数据驱动的三维湍流燃烧高分辨率重构
	[77]	航发涡轮叶片	二维温度场反演	基于热力学控制方程损失项，提高温度场预测精度
	[78]	旋转爆震发动机	燃烧室流场重构	基于少量观测数据，完成燃烧室流场全维高分辨率重构
	[79]	航发RUL预测	航发故障模式识别	结合自注意力机制，在C-MAPSS数据集验证有效性
	[81]	航发RUL预测	信息处理与特征提取	引入基于物理信息的自注意力编码器，在C-MAPSS数据集验证有效性
	[82]	航发叶-盘系统	疲劳度分析	多场景验证，拓展新应用方向
无人机技术	[109]	四旋翼无人机	无人机动力学建模	结合PINN与后处理工具，为无人机控制器提供新技术途径
	[110]	四旋翼无人机	无人机系统估计	解决高度非线性和测量噪声难题，性能优于扩展卡尔曼滤波器
	[111]	多旋翼无人机	无人机视觉伺服策略	提高处理速度，增强了机载相机与旋翼系统的鲁棒性
	[112]	四旋翼无人机	无人机轨迹跟踪	使用物理引导残差RNN
扑翼动力学	[113]	扑翼流动方程	扑翼气动性能预测	利用混合粗糙数据驱动的PINN，提升解算效率和精度
航空概念设计	[114]	高超音速飞行器	MTOW预测	在损失函数嵌入工程师先验知识

表2

表3

PINN在航天领域的现有研究总结"

应用领域	文献	研究对象	研究内容	创新或成果
航天轨道力学	[85]	LEO卫星	轨道外推	将轨道轨迹建模为PDE，利用深度神经算子保证外推精度
	[87]	卫星与空间碎片	轨道外推	引入物理信息增强微分方程，提升1周内外推精度
	[88]	近地同步轨道卫星	轨道状态估计	学习卫星推力特征、状态向量参数，并充分考虑卫星异常加速度
	[89]	航天器	OT	基于状态方程和BC构建损失
	[90]	航天器	OT	引入X-TFC，将间接法与PINN结合
	[92−95]	航天器	OD	引入ELM，开发PILS，开创智能定轨流程
	[97]	绕月航天器	OD	引入迁移学习，提升OD轨迹计算成功率
	[98]	卫星与空间碎片	空间碎片规避	通过模拟非弹性碰撞事件，针对空间碎片状态估计表现优异
	[99]	航天器	航天器追逃与轨道博弈	首次分析问题弱势方并提出博弈建模建议
	[100]	航天器	小行星规避	结合X-TFC与PINN，优化碰撞规避策略
天体引力与磁场建模	[101]	核心坍缩超新星	湍流与星体爆炸	结合物理信息与卷积神经网络解析磁流体湍流
	[102]	恒星引力场	天体冲击波建模	克服空间多尺度物理环境中损失函数耦合导致的性能不佳
	[103]	地月引力场	引力场建模	引力基础函数紧凑性和效率更高，消除了球谐函数模型的低效
	[104]	小天体引力场	引力场建模	解决布里渊球内发散等问题，提高建模精度和抗噪性能
	[105]	小天体引力场	引力场建模	改进不规则小天体引力分布计算方法
	[106]	太阳磁场	磁场延伸部分测量	将观测数据与无力磁场模型结合，实现日冕磁场高效外推和演化模拟
	[107]	木星磁场	磁场反演	精确解析木星内部磁场结构
航天器能源管理	[115]	锂离子电池	太空环境电池建模	将等效电路模型融入PINN
天文与宇宙学	[116]	脉冲星	三维磁层建模	复现现有研究的多种脉冲星轴对称磁层模型
	[117]	系外行星	大气辐射传输建模	提高行星大气内部吸收和散射现象计算精度
	[118]	宇宙动力学	宇宙重子填充	引入重子转换效率理论，提升重子属性预测精度并复现金属丰度关系

表3

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表4

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类型	文献	方法特点
前馈神经网络	[29]	隐藏层个数为2到4，神经元数量为32。求解地下介质中的水力传导率、水头和浓度场
	[30]	隐藏层个数为5到8，神经元数量为250。用于金属增材制造过程建模，在适量标记数据下准确预测温度和熔池动态
	[31]	采用隐藏层大于9的深层网络，用于解决流体力学问题
循环神经网络	[32]	通过循环神经网络执行常微分方程的数值积分，以有向图的形式表示物理信息核
	[33]	将物理信息融入长短期记忆网络，显著提高线性和非线性结构响应建模的精度，其表现优于传统长短期记忆网络
	[34]	使用长短期记忆-PINN模型研究电池寿命预测问题。在电池温度范围较大时，较传统长短期记忆网络具有更低的预测误差
卷积神经网络	[35]	提出一种基于物理信息约束的卷积神经网络架构，解决不规则域上的参数化PDE问题
	[36]	提出一种用于模拟和预测高度异质性储层模型中的瞬态达西流动的物理信息深度卷积神经网络架构
	[37]	使用结合卷积神经网络和PINN的两阶段混合学习框架，解决全反向电导率成像问题
其他	[38]	提出一种基于生成对抗网络的PINN方法，利用对抗性和物理信息损失的嵌入，求解湍流反应流的亚滤波问题
	[39]	将物理信息加入图神经网络，依靠稀疏传感器数据高效精确地预测氢气射流和扩散行为
	[40]	在能够迅速逼近复杂数据分布的扩散模型^[41]中加入物理约束条件，成功提升生成样本的可靠性

误差指标	X/km	Y/km	Z/km
RMSE	46.01	45.53	0.87
MAE	41.04	40.63	0.71
MaxAE	81.81	79.00	1.73

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