Nonlinear Intrinsic Variables and State Reconstruction in Multiscale Simulations

TL;DR

This paper introduces nonlinear intrinsic variables (NIV) as a method to extract low-dimensional, meaningful representations from high-dimensional multiscale simulation data, aiding in understanding and accelerating complex physical simulations.

Contribution

The paper demonstrates how NIV can merge different simulation data sets, handle partial observations, and infer unmeasured variables, improving analysis of multiscale simulations.

Findings

NIV effectively filters noise and recovers a unique reference frame.

Application to enzyme reaction network reveals slow and fast dynamics.

Molecular dynamics of alanine dipeptide shows NIV captures essential conformational changes.

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.