Extreme events gain the attention of researchers due to their utmost importance in various
contexts ranging from climate to brain. An observable that deviates significantly from its long …

The Covid-19 pandemic has galvanized scientists to apply machine learning methods to
help combat the crisis. Despite the significant amount of research there exists no …

The role of topological heterogeneity in the origin of extreme events in a network is
investigated here. The dynamics of the oscillators associated with the nodes are assumed to …

In this paper, we employ a Deep Learning technique, namely Physics-Informed Neural
Network for solving multi-component Manakov models. In particular, we consider three and …

Glioma grading is critical for treatment selection, and the fine classification between glioma
grades II and III is still a pathological challenge. Traditional systems based on a single deep …

Abstract Machine learning models play a vital role in the prediction task in several fields of
study. In this work, we utilize the ability of machine learning algorithms to predict the …

We investigate the physics informed neural network method, a deep learning approach, to
approximate soliton solution of the nonlinear Schrödinger equation with parity time …

In this topical review, we present a brief overview of the different methods and measures to
detect the occurrence of critical transitions in complex systems. We start by introducing the …

Deep Learning has received increased attention due to its unbeatable success in many
fields, such as computer vision, natural language processing, recommendation systems, and …

Since Galileo's time, the pendulum has evolved into one of the most exciting physical
objects in mathematical modeling due to its vast range of applications for studying various …