This paper proposes a new multi-echelon 4-D chaotic supply chain model (NME-4D-CSCM) incorporating a sinusoidal nonlinearity to better represent uncertainties in supply chain dynamics. The model ...

A new neural architecture converts the classical Volterra series into learnable coefficient tensors, recovering known chaotic dynamics, matching transformer physics, and rivaling black-box forecasters ...

A recurrent neural network trained on simulation data predicts soliton dynamics in Kerr resonators about twenty times faster than traditional Lugiato–Lefever computations.

Many systems obey simple, linear rules: If you pull twice as hard on a spring, it stretches twice as far. However, when we introduce very large forces or complicated interactions, that linear rule ...

A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods ...

Nonlinear dynamics in fluids is a research area focused on the analysis and prediction of fluid flows governed by nonlinear partial differential equations, such as the Navier–Stokes and Euler ...

Engineers have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding ...