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LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS

delete2026-01-01
delete0
PRE
AI
C
Caroline Tatsuoka *
Z
Zhongshu Xu
V
Victor Churchill
D
Dongbin Xiu
DOI：10.1615/JMachLearnModelComput.2026063289delete
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摘要

摘要

En 中文
我们研究了有限时间、部分观测轨迹下的动态系统参数估计问题。与在给定模型结构内校准参数不同，我们将参数估计表述为针对参数-观测算子的反问题，并利用深度神经网络直接从数据中学习其逆映射。在适当的可辨识性假设下，该逆映射是良好定义且连续的，因此适合用神经网络进行逼近。我们讨论了反问题的可辨识性、稳定性和噪声敏感性，并提供了一种简单的误差分解方法，将逼近误差与噪声放大分离。对线性、非线性、非自治、部分观测、含噪声及混沌系统的数值实验表明能够准确恢复参数。对于混沌系统，其恢复效果通过不变动态量的吻合而非逐点轨迹的一致性进行验证。
Keyword:
deep neural networks
inverse problem
parameter estimation

期刊

J
Journal of Machine Learning for Modeling and Computing
IF:
0
论文数:
8
被引数:
0

机构

U
University System of Ohio
学者数:
15.4W
论文数: 13.0W
被引数: 200
 
 ohio state university
学者数:
3.9K
论文数: 2.6K
被引数: 5
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