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- W4285809963 endingPage "104731" @default.
- W4285809963 startingPage "104731" @default.
- W4285809963 abstract "While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle's position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap." @default.
- W4285809963 created "2022-07-19" @default.
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- W4285809963 date "2022-09-01" @default.
- W4285809963 modified "2023-10-05" @default.
- W4285809963 title "Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap" @default.
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- W4285809963 doi "https://doi.org/10.1016/j.isci.2022.104731" @default.
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