Learning Force Fields from Stochastic Trajectories-Reference-Cited by-同舟云学术

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Learning Force Fields from Stochastic Trajectories

Published:2020-04-13 Issue:2 Volume:10 Page:
ISSN:2160-3308
Container-title:Physical Review X
language:en
Short-container-title:Phys. Rev. X

Author:

Frishman Anna,Ronceray Pierre^ORCID

Funder

National Science Foundation

Princeton Center for Theoretical Science

Center for the Physics of Biological Function

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevX.10.021009/fulltext

Reference64 articles.

1. XXVII. A brief account of microscopical observations made in the months of June, July and August 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies

2. ‘Dicty dynamics’:Dictyosteliummotility as persistent random motion

3. Stochastic Differential Equations

4. Stochastic climate models Part I. Theory

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1. Learning minimal representations of stochastic processes with variational autoencoders;Physical Review E;2024-07-18

2. Learning interpretable dynamics of stochastic complex systems from experimental data;Nature Communications;2024-07-17

3. Discovering dynamic laws from observations: The case of self-propelled, interacting colloids;Physical Review E;2024-06-26

4. Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering;Physical Review Research;2024-06-21

5. On Higher Order Drift and Diffusion Estimates for Stochastic SINDy;SIAM Journal on Applied Dynamical Systems;2024-06-14

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