Data-driven parameterization of the generalized Langevin equation
Abstract
Significance The generalized Langevin equation (GLE) provides a precise description of coarse-grained variable dynamics in reduced dimension models. However, computation of the memory kernel poses a major challenge to the practical use of the GLE. This paper presents a data-driven approach to compute the memory kernel, relying on a hierarchy of parameterized rational approximations in terms of the Laplace transform, which can be expanded to arbitrarily high order as necessary. This parameterization makes it convenient to represent the GLE via an extended stochastic model where the memory term is eliminated by properly introducing auxiliary variables. The present method is well-suited for constructing reduced models for nonequilibrium properties of complex systems such as biomolecules, chemical reaction networks, and climate simulations.
- Authors:
-
- Advanced Computing, Mathematics &, Data, Pacific Northwest National Laboratory, Richland, WA 99352,
- Advanced Computing, Mathematics &, Data, Pacific Northwest National Laboratory, Richland, WA 99352,, Division of Applied Mathematics, Brown University, Providence, RI 02912,
- Department of Mathematics, The Pennsylvania State University, University Park, PA 16802
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1333885
- Alternate Identifier(s):
- OSTI ID: 1339841
- Report Number(s):
- PNNL-SA-118385
Journal ID: ISSN 0027-8424
- Grant/Contract Number:
- CM4; AC05-76RL01830
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 113 Journal Issue: 50; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; fluctuation-dissipation theorem; generalized langevin equation; generalized Langevin dynamics; data-driven parameterization; coarse-grained molecular models; reaction rate; model reduction
Citation Formats
Lei, Huan, Baker, Nathan A., and Li, Xiantao. Data-driven parameterization of the generalized Langevin equation. United States: N. p., 2016.
Web. doi:10.1073/pnas.1609587113.
Lei, Huan, Baker, Nathan A., & Li, Xiantao. Data-driven parameterization of the generalized Langevin equation. United States. https://doi.org/10.1073/pnas.1609587113
Lei, Huan, Baker, Nathan A., and Li, Xiantao. Tue .
"Data-driven parameterization of the generalized Langevin equation". United States. https://doi.org/10.1073/pnas.1609587113.
@article{osti_1333885,
title = {Data-driven parameterization of the generalized Langevin equation},
author = {Lei, Huan and Baker, Nathan A. and Li, Xiantao},
abstractNote = {Significance The generalized Langevin equation (GLE) provides a precise description of coarse-grained variable dynamics in reduced dimension models. However, computation of the memory kernel poses a major challenge to the practical use of the GLE. This paper presents a data-driven approach to compute the memory kernel, relying on a hierarchy of parameterized rational approximations in terms of the Laplace transform, which can be expanded to arbitrarily high order as necessary. This parameterization makes it convenient to represent the GLE via an extended stochastic model where the memory term is eliminated by properly introducing auxiliary variables. The present method is well-suited for constructing reduced models for nonequilibrium properties of complex systems such as biomolecules, chemical reaction networks, and climate simulations.},
doi = {10.1073/pnas.1609587113},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 50,
volume = 113,
place = {United States},
year = {Tue Nov 29 00:00:00 EST 2016},
month = {Tue Nov 29 00:00:00 EST 2016}
}
https://doi.org/10.1073/pnas.1609587113
Web of Science
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