研究人员开发了一个名为 Mamba-Assisted Closure (MAC) 的新框架，以改进复杂动力学系统的降阶建模。该方法使用基于 Mamba 的序列模型来预测非马尔可夫闭合项，这对于准确表示未解析变量的动力学至关重要。与粘性 Burgers 方程和 Lorenz '96 系统等基准系统上的现有方法相比，MAC 框架展示了卓越的预测准确性和稳定性。 AI
影响 该框架可以提高各种科学领域复杂系统的模拟效率和准确性。
排序理由 该集群包含一篇详细介绍新建模框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.05371v1 Announce Type: cross Abstract: Reduced-order modeling of high-dimensional dynamical systems is often hindered by the non-Markovian closure term that represents the effect of unresolved variables on the resolved dynamics. Inspired by the Mori--Zwanzig formalism,…
Reduced-order modeling of high-dimensional dynamical systems is often hindered by the non-Markovian closure term that represents the effect of unresolved variables on the resolved dynamics. Inspired by the Mori--Zwanzig formalism, in which the closure takes the form of a memory f…