Dynamic mode decomposition (DMD) is a factorization and dimensionality reduction
technique for data sequences. In its most common form, it processes high-dimensional …

In the past as well as present wireless communication systems, the wireless propagation
environment is regarded as an uncontrollable black box that impairs the received signal …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

The idea of federated learning is to collaboratively train a neural network on a server. Each
user receives the current weights of the network and in turns sends parameter updates …

Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …

In this paper, we propose a novel deep convolutional neural network (CNN)-based
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …

Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …

The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …