Multiscale Gentlest Ascent Dynamics for Saddle Point in Effective Dynamics of Slow-Fast System
This work addresses the computational challenge of finding transition states on free energy surfaces in chemical physics, but the results are incremental as it adapts existing methods to a specific multiscale setting.
The authors developed a multiscale method based on gentlest ascent dynamics to compute saddle points in effective dynamics of slow-fast stochastic systems, demonstrating its efficiency on stochastic ODEs and PDEs.
Here we present a multiscale method to calculate the saddle point associated with the effective dynamics arising from a stochastic system which couples slow deterministic drift and fast stochastic dynamics. This problem is motivated by the transition states on free energy surfaces in chemical physics. Our method is based on the gentlest ascent dynamics which couples the position variable and the direction variable and has the local convergence to saddle points. The dynamics of the direction vector is derived in terms of the covariance function with respective to the equilibrium distribution of the fast stochastic process. We apply the multiscale numerical methods to efficiently solve the obtained multiscale gentlest ascent dynamics, {and discuss the acceleration techniques based on the adaptive idea.} The examples of stochastic ordinary and partial differential equations are presented.