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Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Article provides a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. The authors show that their proposed framework can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
Date:
November 11, 2022
Creator:
Rydzewski, Jakub; Chen, Ming; Ghosh, Tushar K. & Valsson, Omar
System:
The UNT Digital Library