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Authors: M. Luskin, C. Le Bris, T. Lelievre, D. Perez, G. Simpson, and A. Voter
Parallel replica dynamics is a method developed by A. Voter for accelerating the computation of processes characterized by a sequence of infrequent events. Such processes spend much of their time about the minima of an underlying potential, occasionally transitioning into different basins of attraction. The essential idea of parallel replica dynamics is that the exit time distribution from a given well for a single process can be approximated by the minimum of the exit time distributions of N independent identical processes, each run for only 1/N-th the amount of time.
To further verify the accuracy and efficiency of parallel replica dynamics, we give a theoretical analysis and computational experiments to estimate the error in the decorrelation stage of the algorithm, and we show how this error cascades into the parallel step. Furthermore, we study a dephasing mechanism and prove that it will successfully complete. This is joint work with C. Le Bris, T. Lelievre, D. Perez, G. Simpson, and A. Voter.
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