A machine learning method for real-time numerical simulations of cardiac electromechanics
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A machine learning method for real-time numerical simulations of cardiac electromechanics
Authors
Keywords
Cardiac electromechanics, Machine learning, Reduced order modeling, Global sensitivity analysis, Bayesian parameter estimation
Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 393, Issue -, Pages 114825
Publisher
Elsevier BV
Online
2022-03-19
DOI
10.1016/j.cma.2022.114825
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A cardiac electromechanical model coupled with a lumped-parameter model for closed-loop blood circulation
- (2022) F. Regazzoni et al. JOURNAL OF COMPUTATIONAL PHYSICS
- The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia
- (2022) Matteo Salvador et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium
- (2021) Li Cai et al. Royal Society Open Science
- Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning
- (2021) Stefano Pagani et al. International Journal for Numerical Methods in Biomedical Engineering
- Accelerating the convergence to a limit cycle in 3D cardiac electromechanical simulations through a data-driven 0D emulator
- (2021) F. Regazzoni et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Mathematical and numerical models for the cardiac electromechanical function
- (2021) Luca Dedè et al. Rendiconti Lincei-Matematica e Applicazioni
- POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium
- (2021) Stefania Fresca et al. Frontiers in Physiology
- Personalization of electro-mechanical models of the pressure-overloaded left ventricle: fitting of Windkessel-type afterload models
- (2020) Laura Marx et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats
- (2020) S. Longobardi et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics
- (2020) F. Regazzoni et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Deep learning-based reduced order models in cardiac electrophysiology
- (2020) Stefania Fresca et al. PLoS One
- Biophysically detailed mathematical models of multiscale cardiac active mechanics
- (2020) Francesco Regazzoni et al. PLoS Computational Biology
- Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations
- (2020) Roberto Piersanti et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- An oscillation-free fully staggered algorithm for velocity-dependent active models of cardiac mechanics
- (2020) F. Regazzoni et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Machine learning for fast and reliable solution of time-dependent differential equations
- (2019) F. Regazzoni et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models
- (2019) Pras Pathmanathan et al. Frontiers in Physiology
- Prediction of Left Ventricular Mechanics Using Machine Learning
- (2019) Yaghoub Dabiri et al. Frontiers in Physics
- Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers
- (2019) F. Levrero-Florencio et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model
- (2018) Henrik Finsberg et al. Journal of Computational Science
- Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics
- (2018) Paolo Di Achille et al. Frontiers in Physiology
- Uncertainty quantification of 2 models of cardiac electromechanics
- (2017) Daniel E. Hurtado et al. International Journal for Numerical Methods in Biomedical Engineering
- A monolithic 3D-0D coupled closed-loop model of the heart and the vascular system: Experiment-based parameter estimation for patient-specific cardiac mechanics
- (2017) Marc Hirschvogel et al. International Journal for Numerical Methods in Biomedical Engineering
- Quantifying inter-species differences in contractile function through biophysical modelling
- (2015) Kristin Tøndel et al. JOURNAL OF PHYSIOLOGY-LONDON
- Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes
- (2013) S. Marchesseau et al. MEDICAL IMAGE ANALYSIS
- A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models
- (2012) J. D. Bayer et al. ANNALS OF BIOMEDICAL ENGINEERING
- Global sensitivity measures from given data
- (2012) Elmar Plischke et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- An analysis of deformation-dependent electromechanical coupling in the mouse heart
- (2012) Sander Land et al. JOURNAL OF PHYSIOLOGY-LONDON
- Whole-Heart Modeling
- (2011) Natalia A. Trayanova CIRCULATION RESEARCH
- Estimation of global sensitivity indices for models with dependent variables
- (2011) S. Kucherenko et al. COMPUTER PHYSICS COMMUNICATIONS
- A local sensitivity analysis method for developing biological models with identifiable parameters: Application to cardiac ionic channel modelling
- (2011) Anna A. Sher et al. Future Generation Computer Systems-The International Journal of eScience
- Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter
- (2011) Jiahe Xi et al. Journal of the Mechanical Behavior of Biomedical Materials
- Cardiac cell modelling: Observations from the heart of the cardiac physiome project
- (2010) Martin Fink et al. PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY
- Cardiac Electromechanics: The Effect of Contraction Model on the Mathematical Problem and Accuracy of the Numerical Scheme
- (2010) P. Pathmanathan et al. QUARTERLY JOURNAL OF MECHANICS AND APPLIED MATHEMATICS
- Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function
- (2009) Vicky Y. Wang et al. MEDICAL IMAGE ANALYSIS
- The arterial Windkessel
- (2008) Nico Westerhof et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Effect of bundle branch block on cardiac output: A whole heart simulation study
- (2008) Edward J. Vigmond et al. PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY
- Uncertainty and sensitivity analysis for models with correlated parameters
- (2007) Chonggang Xu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started