259 related articles for article (PubMed ID: 33599106)
1. Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning.
Pagani S; Manzoni A
Int J Numer Method Biomed Eng; 2021 Jun; 37(6):e3450. PubMed ID: 33599106
[TBL] [Abstract][Full Text] [Related]
2. Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation.
Cicci L; Fresca S; Manzoni A; Quarteroni A
Int J Numer Method Biomed Eng; 2024 Jan; 40(1):e3783. PubMed ID: 37921217
[TBL] [Abstract][Full Text] [Related]
3. Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance.
Barahona J; Sahli Costabal F; Hurtado DE
Comput Methods Programs Biomed; 2024 Jan; 243():107888. PubMed ID: 37948910
[TBL] [Abstract][Full Text] [Related]
4. Propagation of Parametric Uncertainty in Aliev-Panfilov Model of Cardiac Excitation.
Son J; Du Y; Du D
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5450-5453. PubMed ID: 30441570
[TBL] [Abstract][Full Text] [Related]
5. Numerical study of POD-Galerkin-DEIM reduced order modeling of cardiac monodomain formulation.
Khan R; Ng KT
Biomed Phys Eng Express; 2021 Dec; 8(1):. PubMed ID: 34808611
[TBL] [Abstract][Full Text] [Related]
6. Generalized polynomial chaos-based uncertainty quantification and propagation in multi-scale modeling of cardiac electrophysiology.
Hu Z; Du D; Du Y
Comput Biol Med; 2018 Nov; 102():57-74. PubMed ID: 30248513
[TBL] [Abstract][Full Text] [Related]
7. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.
Dhamala J; Arevalo HJ; Sapp J; Horácek BM; Wu KC; Trayanova NA; Wang L
Med Image Anal; 2018 Aug; 48():43-57. PubMed ID: 29843078
[TBL] [Abstract][Full Text] [Related]
8. Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models.
Pathmanathan P; Cordeiro JM; Gray RA
Front Physiol; 2019; 10():721. PubMed ID: 31297060
[TBL] [Abstract][Full Text] [Related]
9. A numerical study on the effects of spatial and temporal discretization in cardiac electrophysiology.
Woodworth LA; Cansız B; Kaliske M
Int J Numer Method Biomed Eng; 2021 May; 37(5):e3443. PubMed ID: 33522111
[TBL] [Abstract][Full Text] [Related]
10. POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium.
Fresca S; Manzoni A; Dedè L; Quarteroni A
Front Physiol; 2021; 12():679076. PubMed ID: 34630131
[TBL] [Abstract][Full Text] [Related]
11. Deep learning-based reduced order models in cardiac electrophysiology.
Fresca S; Manzoni A; Dedè L; Quarteroni A
PLoS One; 2020; 15(10):e0239416. PubMed ID: 33002014
[TBL] [Abstract][Full Text] [Related]
12. Uncertainty quantification of fast sodium current steady-state inactivation for multi-scale models of cardiac electrophysiology.
Pathmanathan P; Shotwell MS; Gavaghan DJ; Cordeiro JM; Gray RA
Prog Biophys Mol Biol; 2015 Jan; 117(1):4-18. PubMed ID: 25661325
[TBL] [Abstract][Full Text] [Related]
13. Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.
Lazarus A; Dalton D; Husmeier D; Gao H
Biomech Model Mechanobiol; 2022 Jun; 21(3):953-982. PubMed ID: 35377030
[TBL] [Abstract][Full Text] [Related]
14. Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics.
Pathmanathan P; Galappaththige SK; Cordeiro JM; Kaboudian A; Fenton FH; Gray RA
Front Physiol; 2020; 11():585400. PubMed ID: 33329034
[TBL] [Abstract][Full Text] [Related]
15. Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.
Pineda-Antunez C; Seguin C; van Duuren LA; Knudsen AB; Davidi B; Nascimento de Lima P; Rutter C; Kuntz KM; Lansdorp-Vogelaar I; Collier N; Ozik J; Alarid-Escudero F
Med Decis Making; 2024 Jun; ():272989X241255618. PubMed ID: 38858832
[TBL] [Abstract][Full Text] [Related]
16. Data-driven Uncertainty Quantification in Computational Human Head Models.
Upadhyay K; Giovanis DG; Alshareef A; Knutsen AK; Johnson CL; Carass A; Bayly PV; Shields MD; Ramesh KT
Comput Methods Appl Mech Eng; 2022 Aug; 398():. PubMed ID: 37994358
[TBL] [Abstract][Full Text] [Related]
17. Sensitivity analysis of an electrophysiology model for the left ventricle.
Del Corso G; Verzicco R; Viola F
J R Soc Interface; 2020 Oct; 17(171):20200532. PubMed ID: 33109017
[TBL] [Abstract][Full Text] [Related]
18. Choosing a Metamodel of a Simulation Model for Uncertainty Quantification.
de Carvalho TM; van Rosmalen J; Wolff HB; Koffijberg H; Coupé VMH
Med Decis Making; 2022 Jan; 42(1):28-42. PubMed ID: 34098793
[TBL] [Abstract][Full Text] [Related]
19. Fast uncertainty quantification of activation sequences in patient-specific cardiac electrophysiology meeting clinical time constraints.
Quaglino A; Pezzuto S; Koutsourelakis PS; Auricchio A; Krause R
Int J Numer Method Biomed Eng; 2018 Jul; 34(7):e2985. PubMed ID: 29577657
[TBL] [Abstract][Full Text] [Related]
20. Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics.
Xie J; Yao B
Comput Biol Med; 2022 Jul; 146():105586. PubMed ID: 35751197
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]