Adaptive Finite Element Solution of Multiscale PDE-ODE Systems . Johansson, A., Chaudhry, J., Carey, V., Estep, D., Ginting, V., Larson, M., & Tavener, S. Computer Methods in Applied Mechanics and Engineering , 287:150-171, 2015.
Adaptive Finite Element Solution of Multiscale PDE-ODE Systems  [link]Paper  abstract   bibtex   
Abstract We consider adaptive finite element methods for a multiscale system consisting of a macroscale model comprising a system of reaction–diffusion partial differential equations coupled to a microscale model comprising a system of nonlinear ordinary differential equations. A motivating example is modeling the electrical activity of the heart taking into account the chemistry inside cells in the heart. Such multiscale models are computationally challenging due to the multiple scales in time and space that are involved. We describe a mathematically consistent approach to couple the microscale and macroscale models based on introducing an intermediate “coupling scale”. Since the ordinary differential equations are defined on a much finer spatial scale than the finite element discretization for the partial differential equation, we introduce a Monte Carlo approach to sampling the fine scale ordinary differential equations. We derive goal-oriented a posteriori error estimates for quantities of interest computed from the solution of the multiscale model using adjoint problems and computable residuals. We distinguish the errors in time and space for the partial differential equation and the ordinary differential equations separately and include errors due to the transfer of the solutions between the equations. The estimate also includes terms reflecting the sampling of the microscale model. Based on the accurate error estimates, we devise an adaptive solution method using a “blockwise” approach. The method and estimates are illustrated using a realistic problem.
@article{Johansson2015150,
title = "Adaptive {F}inite {E}lement {S}olution of {M}ultiscale {PDE}-{ODE} {S}ystems ",
journal = "Computer Methods in Applied Mechanics and Engineering ",
volume = "287",
number = "",
pages = "150-171",
year = "2015",
note = "",
issn = "0045-7825",
url = "http://www.sciencedirect.com/science/article/pii/S0045782515000237",
author = "A. Johansson and J. Chaudhry and V. Carey and D. Estep and V. Ginting and M. Larson and S. Tavener",
keywords = "A posteriori error analysis",
keywords = "Adaptive error control",
keywords = "Coupled physics",
keywords = "Multiscale model ",
abstract = "Abstract We consider adaptive finite element methods for a multiscale system consisting of a macroscale model comprising a system of reaction–diffusion partial differential equations coupled to a microscale model comprising a system of nonlinear ordinary differential equations. A motivating example is modeling the electrical activity of the heart taking into account the chemistry inside cells in the heart. Such multiscale models are computationally challenging due to the multiple scales in time and space that are involved. We describe a mathematically consistent approach to couple the microscale and macroscale models based on introducing an intermediate “coupling scale”. Since the ordinary differential equations are defined on a much finer spatial scale than the finite element discretization for the partial differential equation, we introduce a Monte Carlo approach to sampling the fine scale ordinary differential equations. We derive goal-oriented a posteriori error estimates for quantities of interest computed from the solution of the multiscale model using adjoint problems and computable residuals. We distinguish the errors in time and space for the partial differential equation and the ordinary differential equations separately and include errors due to the transfer of the solutions between the equations. The estimate also includes terms reflecting the sampling of the microscale model. Based on the accurate error estimates, we devise an adaptive solution method using a “blockwise” approach. The method and estimates are illustrated using a realistic problem. "
}

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