In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

In this study, we introduce a radial basis function neural network (RBFNN) algorithm. The
proposed architecture is employed to solve the inverse Cauchy problems of the Laplace …

Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …

We apply modern methods in computational topology to the task of discovering and
characterizing phase transitions. As illustrations, we apply our method to four two …

Fully automated classification methods that provide direct physical insights into phase
diagrams are of current interest. Interpretable, ie, fully explainable, methods are desired for …

This paper extends a deep neural network method, a semi-supervised one, to solve
backward heat conduction problems which have been long-standing computational …

It is a hot topic how entanglement, a quantity from quantum information theory, can assist
machine learning. In this work, we implement numerical experiments to classify …