Speaker
Description
Many engineering applications involve the global dynamical analysis of nonlinear systems to explore their fixed points, periodic orbits, or chaotic behavior. The Simple Cell Mapping (SCM) algorithm is a tool for global dynamical analysis relying on the discretization of the state space, resulting in a finite set of cells corresponding to the possible states of the system and a discrete mapping representing the state transitions.
The computational cost of cell mapping-based methods can significantly increase in cases where higher resolution is required due to the complex dynamical behavior of the investigated system or a higher-dimensional system is considered.
This talk presents a possible solution for accelerating the SCM algorithm using parallel computing techniques. We present the challenges of a GPU-based implementation and other potential alternatives for efficient global dynamical analysis.
Supported by the ÚNKP-23-3-1-BME-63 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.
