Abstract

Data assimilation in dynamical networks is intrinsically challenging. A method is introduced for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary environment, using a homogeneous model network to expedite the accurate reconstruction of parameters and unobserved variables. An implementation using ensemble Kalman filtering to track the states of the heterogeneous network is demonstrated on simulated data and applied to a mammalian brain network experiment. The approach has broad applicability for the prediction and control of biological and physical networks.

Original languageEnglish (US)
Article number051909
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume79
Issue number5
DOIs
StatePublished - May 13 2009

Fingerprint

Data Assimilation
Heterogeneous Networks
assimilation
Kalman Filtering
Ensemble
brain
Prediction
Cell
oscillators
Experiment
predictions
cells

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

@article{e05ba8c8aee34327bc82a996d6fecda2,
title = "Data assimilation for heterogeneous networks: The consensus set",
abstract = "Data assimilation in dynamical networks is intrinsically challenging. A method is introduced for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary environment, using a homogeneous model network to expedite the accurate reconstruction of parameters and unobserved variables. An implementation using ensemble Kalman filtering to track the states of the heterogeneous network is demonstrated on simulated data and applied to a mammalian brain network experiment. The approach has broad applicability for the prediction and control of biological and physical networks.",
author = "Sauer, {Timothy D.} and Schiff, {Steven J.}",
year = "2009",
month = "5",
day = "13",
doi = "10.1103/PhysRevE.79.051909",
language = "English (US)",
volume = "79",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "5",

}

Data assimilation for heterogeneous networks : The consensus set. / Sauer, Timothy D.; Schiff, Steven J.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 79, No. 5, 051909, 13.05.2009.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Data assimilation for heterogeneous networks

T2 - The consensus set

AU - Sauer, Timothy D.

AU - Schiff, Steven J.

PY - 2009/5/13

Y1 - 2009/5/13

N2 - Data assimilation in dynamical networks is intrinsically challenging. A method is introduced for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary environment, using a homogeneous model network to expedite the accurate reconstruction of parameters and unobserved variables. An implementation using ensemble Kalman filtering to track the states of the heterogeneous network is demonstrated on simulated data and applied to a mammalian brain network experiment. The approach has broad applicability for the prediction and control of biological and physical networks.

AB - Data assimilation in dynamical networks is intrinsically challenging. A method is introduced for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary environment, using a homogeneous model network to expedite the accurate reconstruction of parameters and unobserved variables. An implementation using ensemble Kalman filtering to track the states of the heterogeneous network is demonstrated on simulated data and applied to a mammalian brain network experiment. The approach has broad applicability for the prediction and control of biological and physical networks.

UR - http://www.scopus.com/inward/record.url?scp=67149116902&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67149116902&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.79.051909

DO - 10.1103/PhysRevE.79.051909

M3 - Article

C2 - 19518482

AN - SCOPUS:67149116902

VL - 79

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 5

M1 - 051909

ER -