Efficient estimation of cardiac conductivities via POD-DEIM model order reduction

被引:28
|
作者
Yang, Huanhuan [1 ]
Veneziani, Alessandro [1 ]
机构
[1] Emory Univ, Dept Math & Comp Sci, Atlanta, GA 30322 USA
基金
美国国家科学基金会;
关键词
Cardiac conductivity; Monodomain model; Proper orthogonal decomposition; Discrete empirical interpolation; POSTERIORI ERROR ESTIMATION; INTERPOLATION METHOD; ELECTROPHYSIOLOGY; IDENTIFICATION; APPROXIMATION; ALGORITHM; VALUES; TISSUE; HEART;
D O I
10.1016/j.apnum.2017.01.006
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Clinical oriented applications of computational electrocardiology require efficient and reliable identification of patient-specific parameters of mathematical models based on available measures. In particular, the estimation of cardiac conductivities in models of potential propagation is crucial, since they have major quantitative impact on the solution. Available estimates of cardiac conductivities are significantly diverse in the literature and the definition of experimental/mathematical estimation techniques is an open problem with important practical implications in clinics. We have recently proposed a methodology based on a variational procedure, where the reliability is confirmed by numerical experiments. In this paper we explore model-order-reduction techniques to fit the estimation procedure into timelines of clinical interest. Specifically We consider the Monodomain model and resort to Proper Orthogonal Decomposition (POD) techniques to take advantage of an offline step when solving iteratively the electrocardiological forward model online. In addition, we perform the Discrete Empirical Interpolation Method (DEIM) to tackle the nonlinearity of the model. While standard POD techniques usually fail in this kind of problems, due to the wave-front propagation dynamics, an educated novel sampling of the parameter space based on the concept of Domain of Effectiveness introduced here dramatically reduces the computational cost of the inverse solver by at least 95%. (C) 2017 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:180 / 199
页数:20
相关论文
共 50 条
  • [1] POD-DEIM model order reduction for strain-softening viscoplasticity
    Ghavamian, F.
    Tiso, P.
    Simone, A.
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2017, 317 : 458 - 479
  • [2] Nonlinear model-order reduction for oscillator flows using POD-DEIM
    Fosas de Pando, Miguel
    Schmid, Peter J.
    Sipp, Denis
    IUTAM-ABCM SYMPOSIUM ON LAMINAR TURBULENT TRANSITION, 2015, 14 : 329 - 336
  • [3] Nonlinear Model Reduction for Truss Frame Based on POD-DEIM
    Cui, Chunchun
    Gong, Youping
    Yu, Jiatian
    Ke, Jiang
    Gao, Longbiao
    Chen, Huipeng
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELLING AND STATISTICS APPLICATION (AMMSA 2017), 2017, 141 : 46 - 50
  • [4] Fast Multiscale Reservoir Simulations With POD-DEIM Model Reduction
    Yang, Yanfang
    Ghasemi, Mohammadreza
    Gildin, Eduardo
    Efendiev, Yalchin
    Calo, Victor
    SPE JOURNAL, 2016, 21 (06): : 2141 - 2154
  • [5] POD-DEIM model order reduction for nonlinear heat and moisture transfer in building materials
    Hou, Tianfeng
    Meerbergen, Karl
    Roels, Staf
    Janssen, Hans
    JOURNAL OF BUILDING PERFORMANCE SIMULATION, 2020, 13 (06) : 645 - 661
  • [6] POD-DEIM model order reduction technique for model predictive control in continuous chemical processing
    Van Bo Nguyen
    Si Bui Quang Tran
    Khan, Saif A.
    Rong, Jiawei
    Lou, Jing
    COMPUTERS & CHEMICAL ENGINEERING, 2020, 133
  • [7] POD-DEIM reduction of computational EMG models
    Mordhorst, M.
    Strecker, T.
    Wirtz, D.
    Heidlauf, T.
    Roehrle, O.
    JOURNAL OF COMPUTATIONAL SCIENCE, 2017, 19 : 86 - 96
  • [8] POD-DEIM for efficient reduction of a dynamic 2D catalytic reactor model
    Bremer, Jens
    Goyal, Pawan
    Feng, Lihong
    Benner, Peter
    Sundmacher, Kai
    COMPUTERS & CHEMICAL ENGINEERING, 2017, 106 : 777 - 784
  • [9] A STATE SPACE ERROR ESTIMATE FOR POD-DEIM NONLINEAR MODEL REDUCTION
    Chaturantabut, Saifon
    Sorensen, Danny C.
    SIAM JOURNAL ON NUMERICAL ANALYSIS, 2012, 50 (01) : 46 - 63
  • [10] POD-DEIM based model order reduction for speed-up of flow parametric studies
    Isoz, Martin
    OCEAN ENGINEERING, 2019, 186