Tree tensor networks (TTNs) provide a compact and structured representation
of high-dimensional data, making them valuable in various areas of
computational mathematics and physics. In this paper, we present a rigorous
mathematical framework for expressing high-order derivatives of functional
TTNs, both with or without constraints. Our framework decomposes the total
derivative of a given TTN into a summation of TTNs, each corresponding to the
partial derivatives of the original TTN. Using this decomposition, we derive
the Taylor expansion of vector-valued functions subject to ordinary
differential equation constraints or algebraic constraints imposed by
Runge–Kutta (RK) methods. As a concrete application, we employ this framework
to construct order conditions for RK methods. Due to the intrinsic tensor
properties of partial derivatives and the separable tensor structure in RK
methods, the Taylor expansion of numerical solutions can be obtained in a
manner analogous to that of exact solutions using tensor operators. This
enables the order conditions of RK methods to be established by directly
comparing the Taylor expansions of the exact and numerical solutions,
eliminating the need for mathematical induction. For a given function
$\vector{f}$, we derive sharper order conditions that go beyond the classical
ones, enabling the identification of situations where a standard RK scheme of
order {\it p} achieves unexpectedly higher convergence order for the particular
function. These results establish new connections between tensor network theory
and classical numerical methods, potentially opening new avenues for both
analytical exploration and practical computation.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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