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Learning Dissipative Hamiltonian Dynamics Using Reproducing Kernel Hilbert Spaces and Random Fourier Features

💡 原文英文，约100词，阅读约需1分钟。

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内容提要

该研究提出了一种新方法，通过将向量场分解为辛向量场和耗散向量场，从有限且噪声数据集中学习耗散哈密顿动力学，显著提高了预测精度。

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