Reconstruction of stochastic nonlinear dynamical models from trajectory measurements

TL;DR

This paper introduces an analytical algorithm for reconstructing stochastic nonlinear dynamical models from noisy time-series data, offering robustness and efficiency without extensive parameter searches.

Contribution

The paper presents a novel analytical method for model reconstruction that effectively handles dynamical noise and is applicable to complex systems.

Findings

Successfully inferred parameters of stochastic Lorenz system

Demonstrated robustness across various dynamical models

Achieved high accuracy in reconstructing coupled noisy oscillators

Abstract

A new algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally- and locally-coupled noisy oscillators.