Accurate Memory Kernel Extraction from Discretized Time Series Data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911858950144000 |
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| author | Tepper, Lucas Dalton, Benjamin Netz, Roland R. |
| author_facet | Tepper, Lucas Dalton, Benjamin Netz, Roland R. |
| contents | Memory effects emerge as a fundamental consequence of dimensionality reduction when low-dimensional observables are used to describe the dynamics of complex many-body systems. In the context of molecular dynamics (MD) data analysis, accounting for memory effects using the framework of the generalized Langevin equation (GLE) has proven efficient, accurate and insightful, particularly when working with high-resolution time series data. However, in experimental systems, high-resolution data is often unavailable, raising questions about the impact of the data resolution on the estimated GLE parameters. This study demonstrates that direct memory extraction remains accurate when the discretization time is below the memory time. To obtain memory functions reliably even when the discretization time exceeds the memory time, we introduce a Gaussian Process Optimization (GPO) scheme. This scheme minimizes the deviation of discretized two-point correlation functions between MD and GLE simulations and is able to estimate accurate memory kernels as long as the discretization time stays below the longest time scale in the data, typically the barrier crossing time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09249 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accurate Memory Kernel Extraction from Discretized Time Series Data Tepper, Lucas Dalton, Benjamin Netz, Roland R. Data Analysis, Statistics and Probability Computational Physics Memory effects emerge as a fundamental consequence of dimensionality reduction when low-dimensional observables are used to describe the dynamics of complex many-body systems. In the context of molecular dynamics (MD) data analysis, accounting for memory effects using the framework of the generalized Langevin equation (GLE) has proven efficient, accurate and insightful, particularly when working with high-resolution time series data. However, in experimental systems, high-resolution data is often unavailable, raising questions about the impact of the data resolution on the estimated GLE parameters. This study demonstrates that direct memory extraction remains accurate when the discretization time is below the memory time. To obtain memory functions reliably even when the discretization time exceeds the memory time, we introduce a Gaussian Process Optimization (GPO) scheme. This scheme minimizes the deviation of discretized two-point correlation functions between MD and GLE simulations and is able to estimate accurate memory kernels as long as the discretization time stays below the longest time scale in the data, typically the barrier crossing time. |
| title | Accurate Memory Kernel Extraction from Discretized Time Series Data |
| topic | Data Analysis, Statistics and Probability Computational Physics |
| url | https://arxiv.org/abs/2401.09249 |