Deep Learning Method for Computing Committor Functions with Adaptive Sampling

arXiv:2404.06206v1 Announce Type: cross
Abstract: The committor function is a central object for quantifying the transitions between metastable states of dynamical systems. Recently, a number of computational methods based on deep neural networks have been developed for computing the high-dimensional committor function. The success of the methods relies on sampling adequate data for the transition, which still is a challenging task for complex systems at low temperatures. In this work, we propose a deep learning method with two novel adaptive …

abstract arxiv computational computing cs.lg cs.na deep learning function functions math.na networks neural networks object physics.comp-ph sampling success systems transitions type