English(EN) Learning symplectic model reduction based on a approximation theorem of symplectic embeddings

新型自编码器在模型降阶中保留辛结构

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-04 04:00

研究人员开发了一种新的方法，用于在保留其基本辛结构的同时降低复杂哈密顿系统的维度。这种方法称为保持辛性的自编码器（SpAE），它使用一种特定的神经网络架构，保证潜在坐标支持哈密顿流，从而提高长期预测的准确性。在粒子和晶格系统上的实验表明，SpAE在重建和预测方面均优于标准自编码器。 AI

影响该方法可以提高复杂物理系统模拟的长期稳定性和准确性。

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arXiv cs.LG TIER_1 English(EN) · Liyi Feng, Yifa Tang, Yulin Xie, Ruili Zhang, Aiqing Zhu · 2026-06-04 04:00

基于辛嵌入近似定理的学习辛模型降阶

arXiv:2606.04623v1 Announce Type: new Abstract: High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics evolving on symplectic manifolds. Although deep learning provides powerful tools for constructing low-dimensional…