Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

When simulating a mechanism from science or engineering, or an industrial process, one is
frequently required to construct a mathematical model, and then resolve this model …

Solving Sylvester equation is a common algebraic problem in mathematics and control
theory. Different from the traditional fixed-parameter recurrent neural networks, such as …

A novel recurrent neural network, which is named as complex varying-parameter convergent-
differential neural network (CVP-CDNN), is proposed in this paper for solving the time …

We propose a parallel version of the cross interpolation algorithm and apply it to calculate
high-dimensional integrals motivated by Ising model in quantum physics. In contrast to …

Zeroing neural network (ZNN) is an effective neural solution to time-varying problems,
including time-varying complex Sylvester equations. Generally, a ZNN model involves a …

Many problems in science and technology can be cast using differential equations with both
fractional time and spatial derivatives. To accurately simulate natural phenomena using this …

The dynamic Sylvester equation (DSE) is frequently encountered in engineering and
mathematics fields. The original zeroing neural network (OZNN) can work well to handle …

The goal of this paper is the efficient numerical simulation of optimization problems
governed by either steady-state or unsteady partial differential equations involving random …

In this article, two Adams–Bashforth-type integration-enhanced discrete-time zeroing neural
dynamic (ADTIZD) models are proposed to solve the time-varying complex Sylvester …