Linking discrete and continuum diffusion models: Well‐posedness and stable finite element discretizations

In the context of mathematical modeling, it is sometimes convenient to integrate models of different nature. These types of combinations, however, might entail difficulties even when individual models are well‐understood, particularly in relation to the well‐posedness of the ensemble. In this article, we focus on combining two classes of dissimilar diffusive models: the first one defined over a continuum and the second one based on discrete equations that connect average values of the solution over disjoint subdomains. For stationary problems, we show unconditional stability of the linked problems and then the stability and convergence of its discretized counterpart when mixed finite elements are used to approximate the model on the continuum. The theoretical results are highlighted with numerical examples illustrating the effects of linking diffusive models. As a side result, we show that the methods introduced in this article can be used to infer the solution of diffusive problems with incomplete data.

[1]  A. Popp,et al.  One-way coupled fluid–beam interaction: capturing the effect of embedded slender bodies on global fluid flow and vice versa , 2022, Advanced Modeling and Simulation in Engineering Sciences.

[2]  Gabriel F. Barros,et al.  Modeling nonlocal behavior in epidemics via a reaction–diffusion system incorporating population movement along a network , 2022, Computer Methods in Applied Mechanics and Engineering.

[3]  Alexander Popp,et al.  Consistent coupling of positions and rotations for embedding 1D Cosserat beams into 3D solid volumes , 2021, Computational Mechanics.

[4]  Nora Hagmeyer,et al.  One-way coupled fluid–beam interaction: capturing the effect of embedded slender bodies on global fluid flow and vice versa , 2021, Adv. Model. Simul. Eng. Sci..

[5]  Nicola Guglielmi,et al.  Delay differential equations for the spatially resolved simulation of epidemics with specific application to COVID‐19 , 2021, Mathematical methods in the applied sciences.

[6]  Gabriel F. Barros,et al.  Assessing the Spatio-temporal Spread of COVID-19 via Compartmental Models with Diffusion in Italy, USA, and Brazil , 2021, Archives of Computational Methods in Engineering.

[7]  T. Hughes,et al.  Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study , 2020, Computational Mechanics.

[8]  Kent-Andre Mardal,et al.  Analysis and approximation of mixed-dimensional PDEs on 3D-1D domains coupled with Lagrange multipliers , 2020, SIAM J. Numer. Anal..

[9]  Albert Y. Zomaya,et al.  Partial Differential Equations , 2007, Explorations in Numerical Analysis.

[10]  Paolo Zunino,et al.  Derivation and analysis of coupled PDEs on manifolds with high dimensionality gap arising from topological model reduction , 2019, ESAIM: Mathematical Modelling and Numerical Analysis.

[11]  I. Romero Coupling nonlinear beams and continua: Variational principles and finite element approximations , 2018 .

[12]  Alfio Quarteroni,et al.  Geometric multiscale modeling of the cardiovascular system, between theory and practice , 2016 .

[13]  Maia Martcheva,et al.  An Introduction to Mathematical Epidemiology , 2015 .

[14]  Alison L. Marsden,et al.  A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations , 2013, J. Comput. Phys..

[15]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[16]  A. Quarteroni,et al.  On the coupling of 1D and 3D diffusion-reaction equations. Applications to tissue perfusion problems , 2008 .

[17]  A. Furman Modeling Coupled Surface–Subsurface Flow Processes: A Review , 2008 .

[18]  Karan S. Surana,et al.  Transition finite elements for three‐dimensional stress analysis , 1980 .

[19]  E. Kuhl Computational Epidemiology: Data-Driven Modeling of COVID-19 , 2021 .

[20]  L. Evans,et al.  Partial Differential Equations , 2000 .

[21]  Hamilton-Jacobi Equations,et al.  Mixed Finite Element Methods for , 1996 .

[22]  Jean E. Roberts,et al.  Mixed and hybrid methods , 1991 .

[23]  Jean E. Roberts,et al.  Mixed and hybrid finite element methods , 1987 .