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English(EN) Cluster-Weighted EDMD

新的CW-EDMD方法改进了复杂系统的Koopman算子近似 · 跟踪2个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了簇加权扩展动态模式分解（CW-EDMD），一种从数据中近似Koopman算子的新颖方法。该方法通过学习软相空间划分和每个簇的算子，解决了具有不同局部动力学系统的单一全局算子效率低下的问题。CW-EDMD利用期望最大化目标，该目标同时考虑了几何邻近性和预测残差，使簇能够专注于局部Koopman模型准确的地方。在Lorenz、阻尼摆和Duffing系统上的实验表明，与匹配度EDMD相比，误差显著降低，中位数一步误差分别降低了57倍、2.7倍和12倍。 AI

影响 该方法可以增强具有局部动力学的复杂系统的建模，有可能提高各个科学和工程领域的预测能力。

排序理由 该簇包含一篇详细介绍近似Koopman算子新方法的论文。

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新的CW-EDMD方法改进了复杂系统的Koopman算子近似 · 跟踪2个来源

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报道来源 [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Cluster-Weighted EDMD

    Extended Dynamic Mode Decomposition (EDMD) approximates Koopman operators from data, but a single global operator is inefficient when different state-space regions exhibit distinct local dynamics. We introduce Cluster-Weighted EDMD (CW-EDMD), which jointly learns a soft phase-spa…

  2. arXiv stat.ML TIER_1 English(EN) · Lorenzo Tomaz, Judd Rosenblatt, Flavio Kicis, Thomas B. Jones, Diogo Schwerz de Lucena ·

    簇加权EDMD

    arXiv:2607.12243v1 Announce Type: cross Abstract: Extended Dynamic Mode Decomposition (EDMD) approximates Koopman operators from data, but a single global operator is inefficient when different state-space regions exhibit distinct local dynamics. We introduce Cluster-Weighted EDM…

  3. arXiv stat.ML TIER_1 English(EN) · Diogo Schwerz de Lucena ·

    簇加权EDMD

    Extended Dynamic Mode Decomposition (EDMD) approximates Koopman operators from data, but a single global operator is inefficient when different state-space regions exhibit distinct local dynamics. We introduce Cluster-Weighted EDMD (CW-EDMD), which jointly learns a soft phase-spa…

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