Identifying causal relationships from observational time-series data is a central challenge in understanding complex dynamical systems. However, few existing me

Data-driven machine learning has established a robust foundation for reconstructing nonlinear dynamical systems from observations, primarily for the purposes of forecasting and control. However, most ...

Reaction–diffusion (RD) processes on complex networks, along with their associated critical dynamics, have attracted considerable attention and spurred intensiv

Detecting and quantifying causal influence is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much atten...

Given the challenge of modeling multidimensional dynamical systems, which stems from the complexity of their intrinsic interactions and nonlinear dynamics, reservoir computing (RC) offers a robust fra...

Opinion polarization is often considered as the primary driver of social friction, leading to exhaustive efforts to force a consensus. However, new research suggests a more pragmatic goal: reducing th...

In biochemical reaction networks, the first passage time (FPT) of a reaction quantifies the time, from the initial state, that it takes for the reaction to firs