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English(EN) CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems

新的机器学习框架以增强的预测能力学习复杂的物理动力学

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文介绍了一种用于预测复杂物理系统动力学的先进机器学习框架。第一篇论文 CaLiSym 通过将状态嵌入到提升的正则相空间中，将精确的辛学习扩展到与环境有能量交换的系统。第二篇论文 CSympNet-ID 专注于线性阻尼哈密顿系统，提出了一种在强制执行共形辛性的同时学习单步流映射的框架。两种方法都证明了预测精度的提高，特别是在数据稀疏或分布外的情况下，其性能优于非结构化基线。 AI

影响 这些框架推动了物理信息机器学习的发展，能够为复杂的现实世界系统（尤其是在机器人和力学领域）提供更准确、更稳定的预测。

排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了用于物理信息学习的新机器学习框架。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的机器学习框架以增强的预测能力学习复杂的物理动力学

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两篇在 arXiv 上发表的学术论文，详细介绍了用于物理信息学习的新机器学习框架。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares, Ioannis Havoutis, Gerard J. Milburn ·

    CaLiSym：通过结构化正则提升学习真实系统的辛动力学

    arXiv:2607.06824v1 Announce Type: cross Abstract: Physics-informed learning promises data-efficient and stable dynamics prediction, yet its strongest geometric guarantees have largely remained confined to closed conservative systems. This excludes many robotic systems of practica…

  2. arXiv cs.LG TIER_1 English(EN) · Jiale Gong (School of Mathematics), Pengzhan Jin (National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China), Dongyang Kuang (School of Mathematics), Lu Li (School of Mathematics), Yifa Tang (State Key Labo… ·

    CSympNet-ID：用于线性阻尼哈密顿系统的共形辛映射学习

    arXiv:2607.03339v1 Announce Type: new Abstract: Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped Hamiltonian systems, the exact flow is generally not …

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