Chaos (Woodbury, N.Y.)
Jul 1, 2022
Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced-order modeling method that capitalizes on this fact by finding a coordi...
Chaos (Woodbury, N.Y.)
Jul 1, 2022
Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models b...
Chaos (Woodbury, N.Y.)
Jul 1, 2022
Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational res...
Chaos (Woodbury, N.Y.)
Jun 1, 2022
This work presents a recently developed approach based on physics-informed neural networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical kinetic problems with governing equations of stiff ordinary differential ...
Chaos (Woodbury, N.Y.)
Jun 1, 2022
Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically infinite signal...
Chaos (Woodbury, N.Y.)
Jun 1, 2022
Computational modeling and experimental/clinical prediction of the complex signals during cardiac arrhythmias have the potential to lead to new approaches for prevention and treatment. Machine-learning (ML) and deep-learning approaches can be used fo...
Chaos (Woodbury, N.Y.)
May 1, 2022
As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-bas...
Chaos (Woodbury, N.Y.)
Apr 1, 2022
To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data a...
Chaos (Woodbury, N.Y.)
Mar 1, 2022
We have proposed and studied both numerically and experimentally a multistable system based on a self-sustained Van der Pol oscillator coupled to passive oscillatory circuits. The number of passive oscillators determines the number of multistable osc...
Chaos (Woodbury, N.Y.)
Mar 1, 2022
This paper handles the distributed adaptive synchronization problem for a class of unknown second-order nonlinear multiagent systems subject to external disturbance. It is supposed to be an unknown one for the underlying external disorder. First, the...