Test particles in a gas: Markovian and non-Markovian Langevin dynamics

Ferrari, L.

doi:10.1016/j.chemphys.2019.03.011

Cited by 17 publications

(4 citation statements)

References 48 publications

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“…These approaches are not the only ones that can outperform classical analysis methods: in the study of collective phenomena in physical complex systems, Memory Functions Formalism [17] and Flicker-Noise Spectroscopy [18] are also can show significant results. These methods can be applied to analyze autocorrelations, cross-correlations and statistical memory effects in the recorded experimental data [19], which reflect the manifestation of collective phenomena in plasma and during thermonuclear fusion [20,21].…”

Section: Discussionmentioning

confidence: 99%

The study of statistical features of the evolution of complex physical systems using adaptive machine learning methods

Yunusov

Demin

Elenev

2022

J. Phys.: Conf. Ser.

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In this work, we discuss various machine learning methods and their implementation in the field of complex physical systems for the analysis of experimental data. These methods: classical machine learning, neural nets and deep learning allow greatly outperforming classical analysis methods by giving the algorithm the ability to “learn” and perform tasks adapting to the data provided and search. Neural nets and deep learning approaches are used to search for hidden patterns of the suggested input data that can’t be analyzed using common methods. This variety of methods can be applied to study collective phenomena in plasma and thermonuclear fusion on the basis of experimental data of physical experiments with a higher level of performance than classical approaches.

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Section: Discussionmentioning

confidence: 99%

The study of statistical features of the evolution of complex physical systems using adaptive machine learning methods

Yunusov

Demin

Elenev

2022

J. Phys.: Conf. Ser.

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“…The proposed methods can be effectively applied to the study of autocorrelations, crosscorrelations, and statistical memory effects in time sequences and spatial maps of experimentally recorded parameters of physical experiments (see for example [27,28]), including the study of collective effects in plasma [29], the study of surface structures on nano-and microscales [30], the structure of molecules and their complexes according to the characteristic absorption bands.…”

Section: Discussionmentioning

confidence: 99%

The development of statistical analysis methods for the study of correlations and statistical memory effects in the recorded data of physical experiments

Demin

Yunusov

2022

J. Phys.: Conf. Ser.

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In this paper, we discuss the prospects for the use of statistical analysis methods in the study of correlations and statistical memory effects in the experimental data of physical experiments. Developed methods under consideration: Memory Functions Formalism and Flicker-Noise Spectroscopy allow obtaining a large set of quantitative parameters and qualitative characteristics directly from temporal signals generated by complex physical systems. Additionally, for the study of collective phenomena and effects, one- and two-parameter cross-correlation functions are proposed, which allow studying cross-correlations between simultaneously recorded signals in spatially separated areas of the object under study. The introduced analytical relations and numerical algorithms can be applied to solve the problems of metrology of surface structures at the nano- and microlevels, to study collective phenomena in the plasma of astrophysical objects, the structure of molecules, and their complexes.

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“…In addition to the GME, the generalized Langevin equation (GLE) has also been recently applied to study biomolecular dynamics [36][37][38][39][40][41][42][43] . The GLE is a useful tool for investigating the non-Markovian dynamics of biomolecules along one or several collective variables [41][42] .…”

Section: Introductionmentioning

confidence: 99%

Integrative Generalized Master Equation: A Theory to Study Long-timescale Biomolecular Dynamics via the Integrals of Memory Kernels

Cao

Qiu

Kalin

et al. 2023

Preprint

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The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME for biomolecular dynamics, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate protein dynamics using a discretized GME. qMSM can be constructed with much shorter MD simulation trajectories than the Markov State Model (MSM). However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study complicated conformational changes of biomolecules. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of the qMSM in complicated molecules. In this paper, we propose a new theory, the Integrative GME (IGME), to overcome the challenges of the qMSM by using the time integrations of memory kernels. The IGME avoids the numerical instability induced by explicit computation of time-dependent memory kernel functions, giving more robust predictions of long-time dynamics. Using our analytical solution of the IGME, we propose a new approach to compute memory kernels and long-time dynamics in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, the FIP35 WW domain, and Taq RNAP. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate complex conformational changes of biomolecules.

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Test particles in a gas: Markovian and non-Markovian Langevin dynamics

Cited by 17 publications

References 48 publications

The study of statistical features of the evolution of complex physical systems using adaptive machine learning methods

The study of statistical features of the evolution of complex physical systems using adaptive machine learning methods

The development of statistical analysis methods for the study of correlations and statistical memory effects in the recorded data of physical experiments

Integrative Generalized Master Equation: A Theory to Study Long-timescale Biomolecular Dynamics via the Integrals of Memory Kernels

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