Nonlinear intrinsic variables and state reconstruction in multiscale simulations

Talmon, Ronen; Rabin, Neta

doi:10.1063/1.4828457

Title: Nonlinear intrinsic variables and state reconstruction in multiscale simulations

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Abstract

Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.

Authors:

Talmon, Ronen; Rabin, Neta ^[1]

Department of Exact Sciences, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv (Israel)

Publication Date:: Thu Nov 14 00:00:00 EST 2013

OSTI Identifier:: 22251461

Resource Type:: Journal Article

Journal Name:: Journal of Chemical Physics

Additional Journal Information:: Journal Volume: 139; Journal Issue: 18; Other Information: (c) 2013 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-9606

Country of Publication:: United States

Language:: English

Subject:: 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALANINES; COMPUTERIZED SIMULATION; ENZYMES; MOLECULAR DYNAMICS METHOD; MONTE CARLO METHOD; STOCHASTIC PROCESSES

Citation Formats


                    Dsilva, Carmeline J., E-mail: cdsilva@princeton.edu, Talmon, Ronen, Coifman, Ronald R., E-mail: coifman@math.yale.edu, Rabin, Neta, Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544. Nonlinear intrinsic variables and state reconstruction in multiscale simulations.  United States: N. p., 2013. 
        Web.  doi:10.1063/1.4828457.

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                    Dsilva, Carmeline J., E-mail: cdsilva@princeton.edu, Talmon, Ronen, Coifman, Ronald R., E-mail: coifman@math.yale.edu, Rabin, Neta, Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, & Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544. Nonlinear intrinsic variables and state reconstruction in multiscale simulations.  United States.  https://doi.org/10.1063/1.4828457

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                    Dsilva, Carmeline J., E-mail: cdsilva@princeton.edu, Talmon, Ronen, Coifman, Ronald R., E-mail: coifman@math.yale.edu, Rabin, Neta, Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544. 2013.  
        "Nonlinear intrinsic variables and state reconstruction in multiscale simulations".  United States.  https://doi.org/10.1063/1.4828457.

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@article{osti_22251461,

  title        = {Nonlinear intrinsic variables and state reconstruction in multiscale simulations},

  author       = {Dsilva, Carmeline J., E-mail: cdsilva@princeton.edu and Talmon, Ronen and Coifman, Ronald R., E-mail: coifman@math.yale.edu and Rabin, Neta and Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544},

  abstractNote = {Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discuss and illustrate the use of nonlinear intrinsic variables (NIV) in the mining of high-dimensional multiscale simulation data. In particular, we focus on the way NIV allows us to functionally merge different simulation ensembles, and different partial observations of these ensembles, as well as to infer variables not explicitly measured. The approach relies on certain simple features of the underlying process variability to filter out measurement noise and systematically recover a unique reference coordinate frame. We illustrate the approach through two distinct sets of atomistic simulations: a stochastic simulation of an enzyme reaction network exhibiting both fast and slow time scales, and a molecular dynamics simulation of alanine dipeptide in explicit water.},

  doi          = {10.1063/1.4828457},

  url          = {https://www.osti.gov/biblio/22251461},
  journal      = {Journal of Chemical Physics},

  issn         = {0021-9606},

  number       = 18,

  volume       = 139,

  place        = {United States},

  year         = {Thu Nov 14 00:00:00 EST 2013},

  month        = {Thu Nov 14 00:00:00 EST 2013}

}

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