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Data-driven parameterization of the generalized Langevin equation
We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statist...
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| Gepubliceerd in: | Proc Natl Acad Sci U S A |
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| Hoofdauteurs: | , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
National Academy of Sciences
2016
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5167214/ https://ncbi.nlm.nih.gov/pubmed/27911787 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1609587113 |
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