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Considering discrepancy when calibrating a mechanistic electrophysiology
  model
v1v2 (latest)

Considering discrepancy when calibrating a mechanistic electrophysiology model

13 January 2020
Chon Lok Lei
Sanmitra Ghosh
Dominic G. Whittaker
Y. Aboelkassem
K. Beattie
C. Cantwell
T. Delhaas
C. Houston
G. M. Novaes
A. Panfilov
P. Pathmanathan
M. Riabiz
R. W. dos Santos
J. Walmsley
Keith Worden
Gary R. Mirams
Richard D. Wilkinson
ArXiv (abs)PDFHTML

Papers citing "Considering discrepancy when calibrating a mechanistic electrophysiology model"

3 / 3 papers shown
Empirical quantification of predictive uncertainty due to model
  discrepancy by training with an ensemble of experimental designs: an
  application to ion channel kinetics
Empirical quantification of predictive uncertainty due to model discrepancy by training with an ensemble of experimental designs: an application to ion channel kineticsBulletin of Mathematical Biology (Bull. Math. Biol.), 2023
Joseph G. Shuttleworth
Chon Lok Lei
Dominic G. Whittaker
M. Windley
A. Hill
S. Preston
Gary R. Mirams
115
13
0
06 Feb 2023
Physics-informed machine learning for Structural Health Monitoring
Physics-informed machine learning for Structural Health MonitoringStructural Integrity (SI), 2021
E. Cross
S. Gibson
M. R. Jones
D. J. Pitchforth
S. Zhang
T. Rogers
AI4CE
301
48
0
30 Jun 2022
Deep learning-based reduced order models in cardiac electrophysiology
Deep learning-based reduced order models in cardiac electrophysiologyPLoS ONE (PLOS ONE), 2020
S. Fresca
Andrea Manzoni
Luca Dede'
A. Quarteroni
169
74
0
02 Jun 2020
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