Extreme events in dynamical systems and random walkers: A review
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 …
contexts ranging from climate to brain. An observable that deviates significantly from its long …
Extreme events in a complex network: Interplay between degree distribution and repulsive interaction
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 …
investigated here. The dynamics of the oscillators associated with the nodes are assumed to …
[HTML][HTML] Data-driven multi-valley dark solitons of multi-component Manakov model using physics-informed neural networks
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 …
Network for solving multi-component Manakov models. In particular, we consider three and …
[HTML][HTML] Predicting positon solutions of a family of nonlinear Schrödinger equations through deep learning algorithm
We consider a hierarchy of nonlinear Schrödinger equations (NLSEs) and forecast the
evolution of positon solutions using a deep learning approach called Physics Informed …
evolution of positon solutions using a deep learning approach called Physics Informed …
Extreme rotational events in a forced-damped nonlinear pendulum
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 …
objects in mathematical modeling due to its vast range of applications for studying various …
Studying Extreme Events: An Interdisciplinary Review of the Latest Research
While extreme events have been a focus of research for several decades, often centered
around the causes and impacts of meteorological and climatological events, the term has …
around the causes and impacts of meteorological and climatological events, the term has …
Utilizing sequential modeling in collaborative method for flood forecasting
Flood forecasting has been a major challenge in hydrology for decades. A variety of
approaches have been developed, including numerical models and data-driven models …
approaches have been developed, including numerical models and data-driven models …
Data driven soliton solution of the nonlinear Schrödinger equation with certain PT-symmetric potentials via deep learning
We investigate the physics informed neural network method, a deep learning approach, to
approximate soliton solution of the nonlinear Schrödinger equation with parity time …
approximate soliton solution of the nonlinear Schrödinger equation with parity time …
Understanding hurricane effects on forestlands: Land cover changes and salvage logging
Hurricanes can cause catastrophic damage to forestlands, prompting forest owners to
engage in salvage logging to mitigate losses and resulting in a sudden and pronounced …
engage in salvage logging to mitigate losses and resulting in a sudden and pronounced …
[HTML][HTML] A framework for guiding rapid scientific response to extreme environmental events
The ephemeral nature of signals associated with environmental shocks means that the
speed of information gathering is crucial for the successful extraction of valuable scientific …
speed of information gathering is crucial for the successful extraction of valuable scientific …