Before revision:  As the main benefit of this new approach  , stochastic gradient descent type algorithms can be applied in the training of the committor function without the need of computing any  second-order derivatives  . Numerical results are provided to demonstrate the performance of the proposed method.
After revision:  As the main benefit of this new approach , stochastic gradient descent type algorithms can be applied in the training of the committor function without the need of computing any second-order derivatives . Moreover, unlike the previous methods that enforce the boundary conditions through penalty terms, the new method takes into account the boundary conditions automatically . Numerical results are provided to demonstrate the performance of the proposed method.