论文标题
带有应用的Banach空间的离散且连续的Welch界限
Discrete and Continuous Welch Bounds for Banach Spaces with Applications
论文作者
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。
Let $\{τ_j\}_{j=1}^n$ be a collection in a finite dimensional Banach space $\mathcal{X}$ of dimension $d$ and $\{f_j\}_{j=1}^n$ be a collection in $\mathcal{X}^*$ (dual of $\mathcal{X}$) such that $f_j(τ_j) =1$, $\forall 1\leq j\leq n$. Let $n\geq d$ and $\text{Sym}^m(\mathcal{X})$ be the Banach space of symmetric m-tensors. If the operator $ \text{Sym}^m(\mathcal{X})\ni x \mapsto \sum_{j=1}^nf_j^{\otimes m}(x)τ_j ^{\otimes m}\in\text{Sym}^m(\mathcal{X})$ is diagonalizable and its eigenvalues are all non negative, then we prove that \begin{align}\label{WELCHBANACHABSTRACT} \max _{1\leq j,k \leq n, j\neq k}|f_j(τ_k)|^{2m}\geq \max _{1\leq j,k \leq n, j\neq k}|f_j(τ_k)f_k(τ_j)|^m \geq\frac{1}{n-1}\left[\frac{n}{d+m-1\choose m}-1\right], \quad \forall m \in \mathbb{N}. \end{align} When $ \mathcal{X}=\mathcal{H}$ is a Hilbert space, and $f_j$ is defined by $f_j: \mathcal{H}\ni h \mapsto \langle h, τ_j \rangle \in \mathbb{K}$ (where $\mathbb{K}$ is $\mathbb{R}$ or $\mathbb{C}$), $\forall 1 \leq j \leq n$, then Inequality (1) reduces to Welch bounds. Thus Inequality (1) improves 48 years old result obtained by Welch [\textit{IEEE Transactions on Information Theory, 1974}]. We also prove the following continuous version of Inequality (1) under certain conditions for measure spaces: \begin{align}\label{CONTINUOUSWELCHBANACHABSTRACT} \sup _{α, β\in Ω, α\neq β}|f_α(τ_β) |^{2m}\geq \sup _{α, β\in Ω, α\neq β}|f_α(τ_β)f_β(τ_α) |^{m}\geq \frac{1}{(μ\timesμ)((Ω\timesΩ)\setminusΔ)}\left[\frac{ μ(Ω)^2}{d+m-1 \choose m}-(μ\timesμ)(Δ)\right]. \end{align}
