A simple two-variable model of cardiac excitation
By R. Aliev, A. Panfilov. 1996
How researchers see this paper
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- Jingjia Xu
- J. Sapp
- A. Rahimi
- Fei Gao
- M. Horacek
- Linwei Wang
- Paris Perdikaris
- Maizar Raissi
Citations in the corpus
- Robust Transmural Electrophysiological Imaging: Integrating Sparse and Dynamic Physiological Models into ECG-Based Inference (2015, methodology): “Myocardial infarction: 10 cases of myocardial infarction are test, where ventricular action potential is simulated with the model(2) using parameters modified at the region of infarct scar [8].”
- The importance of mechano-electrical feedback and inertia in cardiac electromechanics (2017, unknown): “where the recovery parameters γ , μ1, μ2 and b control the restitution behavior [8].”
- POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (2021, methodology): “…by splitting the data in training and validation To solve the optimization problem (16)-(17) we use the ADAM algorithm Gradient Descent method [52] computing an adaptive approximation of Deep learning-based reduced order modeling where The encoder function of the convolutional AE can then be…”
- Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, methodology): “Here, we use the monodomain equation with an Aliev-Panfilov cellular model [1] to characterize the electrical behavior of cardiac tissue.”
- A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs (2020, unknown): “We now consider a two-dimensional coupled PDE-ODE nonlinear system, in which the Monodomain equation is coupled to the Aliev-Panfilov ionic model [3], ⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎪⎩ ∂u”
- Noninvasive Transmural Electrophysiological Imaging Based on Minimization of Total-Variation Functional (2014, background): “is considered anisotropic in this study, and its parameter values are adopted from [44].”
- Generating Purkinje networks in the human heart (2015, unknown): “in terms of a cubic polynominal, c1φðφ αÞð1 φÞ, where c1 is a scaling parameter and α is the oscillation threshold (Aliev and Panfilov, 1996).”