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Computing committor functions for the study of rare events using deep learning with importance sampling
The committor function is a central object of study in understanding transitions between metastable states in complex systems.
Deep Learning Method for Computing Committor Functions with Adaptive Sampling
In this work, we propose a deep learning method with two novel adaptive sampling schemes (I and II).
Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
In fact, computing the transition pathway is a challenging task for complex and high-dimensional systems.
A Survey on Collaborative DNN Inference for Edge Intelligence
With the vigorous development of artificial intelligence (AI), the intelligent applications based on deep neural network (DNN) change people's lifestyles and the production efficiency.
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning
The potential component of the decomposition gives the generalized potential.
A Data Driven Method for Computing Quasipotentials
The quasipotential is a natural generalization of the concept of energy functions to non-equilibrium systems.
Computing Committor Functions for the Study of Rare Events Using Deep Learning
The committor function is a central object of study in understanding transitions between metastable states in complex systems.
