Learning Force Fields from Stochastic Trajectories
When monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2020-04-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.10.021009 |
| _version_ | 1818568130003206144 |
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| author | Anna Frishman Pierre Ronceray |
| author_facet | Anna Frishman Pierre Ronceray |
| author_sort | Anna Frishman |
| collection | DOAJ |
| description | When monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication channel, that limits the rate at which information about the force field can be extracted from a Brownian trajectory. This capacity provides an upper bound to the system’s entropy production rate and quantifies the rate at which the trajectory becomes distinguishable from pure Brownian motion. We propose a practical and principled method, stochastic force inference, that uses this information to approximate force fields and spatially variable diffusion coefficients. It is data efficient, including in high dimensions, robust to experimental noise, and provides a self-consistent estimate of the inference error. In addition to forces, this technique readily permits the evaluation of out-of-equilibrium currents and the corresponding entropy production with a limited amount of data. |
| first_indexed | 2024-12-14T06:32:02Z |
| format | Article |
| id | doaj.art-1231c0335fa345c895b5ea4c8fbffc68 |
| institution | Directory Open Access Journal |
| issn | 2160-3308 |
| language | English |
| last_indexed | 2024-12-14T06:32:02Z |
| publishDate | 2020-04-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj.art-1231c0335fa345c895b5ea4c8fbffc682022-12-21T23:13:30ZengAmerican Physical SocietyPhysical Review X2160-33082020-04-0110202100910.1103/PhysRevX.10.021009Learning Force Fields from Stochastic TrajectoriesAnna FrishmanPierre RoncerayWhen monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication channel, that limits the rate at which information about the force field can be extracted from a Brownian trajectory. This capacity provides an upper bound to the system’s entropy production rate and quantifies the rate at which the trajectory becomes distinguishable from pure Brownian motion. We propose a practical and principled method, stochastic force inference, that uses this information to approximate force fields and spatially variable diffusion coefficients. It is data efficient, including in high dimensions, robust to experimental noise, and provides a self-consistent estimate of the inference error. In addition to forces, this technique readily permits the evaluation of out-of-equilibrium currents and the corresponding entropy production with a limited amount of data.http://doi.org/10.1103/PhysRevX.10.021009 |
| spellingShingle | Anna Frishman Pierre Ronceray Learning Force Fields from Stochastic Trajectories Physical Review X |
| title | Learning Force Fields from Stochastic Trajectories |
| title_full | Learning Force Fields from Stochastic Trajectories |
| title_fullStr | Learning Force Fields from Stochastic Trajectories |
| title_full_unstemmed | Learning Force Fields from Stochastic Trajectories |
| title_short | Learning Force Fields from Stochastic Trajectories |
| title_sort | learning force fields from stochastic trajectories |
| url | http://doi.org/10.1103/PhysRevX.10.021009 |
| work_keys_str_mv | AT annafrishman learningforcefieldsfromstochastictrajectories AT pierreronceray learningforcefieldsfromstochastictrajectories |