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

dc.contributor.author

Chen, Yu

dc.contributor.author

Cheng, Jin

dc.contributor.author

Gupta, Arvind

dc.contributor.author

Huang, Huaxiong

dc.contributor.author

Xu, Shixin

dc.date.accessioned

2021-10-18T00:53:15Z

dc.date.available

2021-10-18T00:53:15Z

dc.date.issued

2021-07-28

dc.date.updated

2021-10-18T00:53:14Z

dc.description.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.

dc.identifier

rsos210171

dc.identifier.issn

2054-5703

dc.identifier.issn

2054-5703

dc.identifier.uri

https://hdl.handle.net/10161/23926

dc.language

eng

dc.publisher

The Royal Society

dc.relation.ispartof

Royal Society open science

dc.relation.isversionof

10.1098/rsos.210171

dc.subject

Gaussian process

dc.subject

nonlinear ordinary differential equations

dc.subject

parameter inference

dc.subject

partial observations

dc.title

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

dc.type

Journal article

duke.contributor.orcid

Xu, Shixin|0000-0002-8207-7313

pubs.begin-page

210171

pubs.issue

7

pubs.organisational-group

Duke Kunshan University

pubs.organisational-group

Duke Kunshan University Faculty

pubs.organisational-group

Duke

pubs.publication-status

Published

pubs.volume

8

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