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Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations.

ABSTRACT: Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ordinary differential equations with partial observations. Our method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms. By using initial values obtained by Bayesian steps with low sampling numbers, our deterministic optimization algorithm is both accurate, robust and efficient with partial observations and large noise.

SUBMITTER: Chen Y

PROVIDER: S-EPMC8316824 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Numerical method for parameter inference of systems of nonlinear ordinary differential equations with partial observations.

Chen Yu Y Cheng Jin J Gupta Arvind A Huang Huaxiong H Xu Shixin S

Royal Society open science 20210728 7

Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ordinary differential equations with partial observations. Our method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms. By using initial values obtaine ...[more]

PMID: 34350015

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