Quantitative Analysis of Nonlinear MultifidelityOptimization for Inverse Electrophysiology
- The electric conductivity of cardiac tissue determines excitation propagation and is important for quantifying ischemia and scar tissue and for building personalized models. Estimating conductivity distributions from endocardial mapping data is a challenging inverse problem due to the computational complexity of the monodomain equation, which describes the cardiac excitation. For computing a maximum posterior estimate, we investigate different optimization approaches based on adjoint gradient computation: steepest descent, limited memory BFGS, and recursive multilevel trust region methods, which are using mesh hierarchies or heterogeneous model hierarchies. We compare overall performance, asymptotic convergence rate, and pre-asymptotic progress on selected examples in order to assess the benefit of our multifidelity acceleration.
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| Author: | Fatemeh Chegini, Alena KopanicakovaORCiD, Martin WeiserORCiD, Rolf KrauseORCiD |
|---|---|
| Document Type: | In Proceedings |
| Parent Title (English): | Domain Decomposition Methods in Science and Engineering XXVI |
| First Page: | 65 |
| Last Page: | 76 |
| Publisher: | Springer |
| Year of first publication: | 2022 |
