论文标题

部分可观测时空混沌系统的无模型预测

Blowup polynomials and delta-matroids of graphs

论文作者

Choudhury, Projesh Nath, Khare, Apoorva

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

For every finite simple connected graph $G = (V,E)$, we introduce an invariant, its blowup-polynomial $p_G(\{ n_v : v \in V \})$. This is obtained by dividing the determinant of the distance matrix of its blowup graph $G[{\bf n}]$ (containing $n_v$ copies of $v$) by an exponential factor. We show that $p_G({\bf n})$ is indeed a polynomial function in the sizes $n_v$, which is moreover multi-affine and real-stable. This associates a hitherto unexplored delta-matroid to each graph $G$; and we provide a second unexplored one for each tree. As another consequence, we obtain a new characterization of complete multipartite graphs, via the homogenization at $-1$ of $p_G$ being completely/strongly log-concave, i.e., Lorentzian. (These results extend to weighted graphs.) Finally, we show $p_G$ is indeed a graph invariant, i.e., $p_G$ and its symmetries (in the variables ${\bf n}$) recover $G$ and its isometries, respectively.

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