Compressive Transition Path Sampling

Mehmet Süzen

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arXiv · 1804.09781

Compressive Transition Path Sampling

Mehmet Süzen

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Abstract

Algorithms for rare event complex systems simulations are proposed. Compressed Sensing (CS) has {\it revolutionized} our understanding of limits in signal recovery and has forced us to re-define Shannon-Nyquist sampling theorem for sparse recovery. A formalism to reconstruct trajectories and transition paths via CS is illustrated as proposed algorithms. The implication of under-sampling is quite important. This formalism could increase the tractable time-scales {\it immensely} for simulation of statistical mechanical systems and rare event simulations. While, long time-scales are known to be a major hurdle and a challenge for realistic complex simulations for rare events. The outline of how to implement, test and possible challenges on the proposed approach are discussed in detail.

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Mehmet Süzen. 2018-04-21. Compressive Transition Path Sampling. https://arxiv.org/abs/1804.09781

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