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An adaptive rectangular mesh administration and refinement technique with application in cancer invasion models. (English) Zbl 1503.65212

The authors present a new developed mesh structure data administration technique used as machinery for AMR (Aaaptive mesh refinement) on (hyper-)rectangular meshes. The technique is a unified approach for h-refinement on 1-, 2- and 3D domains, which is easy to use and avoids traversing the connectivity graph of the ancestry of mesh cells. Thanks to the employed rectangular mesh structure, the identification of the siblings and the neighbouring cells is simplified. The administration technique is particularly designed for smooth meshes, where the smoothness is dynamically used in the matrix operations. It has a small memory footprint that makes it affordable for a wide range of mesh resolutions over a large class of problems. The capabilities and flexibility of the technique are shown in three applications. The first is a generic experiment in the absence of physical or biological laws where the mesh refinement is dictated by synthetic monitor functions. The second is a physical application of the technique and the AMR in the classical case of the Euler equation. The third application is a biological problem: a 2D tumour growth and invasion of the of the ECM (Extra Cellular Matrix) model.

MSC:

65M50 Mesh generation, refinement, and adaptive methods for the numerical solution of initial value and initial-boundary value problems involving PDEs
65N50 Mesh generation, refinement, and adaptive methods for boundary value problems involving PDEs
65M08 Finite volume methods for initial value and initial-boundary value problems involving PDEs
65Y05 Parallel numerical computation
92C50 Medical applications (general)
92C17 Cell movement (chemotaxis, etc.)
35Q31 Euler equations
35Q92 PDEs in connection with biology, chemistry and other natural sciences

References:

[1] Berger, M. J.; Oliger, J., Adaptive mesh refinement for hyperbolic partial differential equations, J. Comput. Phys., 53, 484-512 (1984) · Zbl 0536.65071
[2] Berger, M. J.; Colella, P., Local adaptive mesh refinement for shock hydrodynamics, J. Comput. Phys., 82, 64-84 (1989) · Zbl 0665.76070
[3] Babuvska, I.; Rheinboldt, W. C., Error estimates for adaptive finite element computations, SIAM J. Numer. Anal., 15, 736-754 (2011) · Zbl 0398.65069
[4] Verfürth, R., A posteriori error estimation and adaptive mesh-refinement techniques, J. Comput. Appl. Math., 50, 1, 67-83 (1994) · Zbl 0811.65089
[5] Teyssier, R., Cosmological hydrodynamics with adaptive mesh refinement, Astron. Astrophys., 385, 1, 337-364 (2002)
[6] Puppo, G.; Semplice, M., Numerical entropy and adaptivity for finite volume schemes, Commun. Comput. Phys., 10, 5, 1132-1160 (2011) · Zbl 1373.76140
[7] Tenaud, C.; Duarte, M., Tutorials on adaptive multiresolution for mesh refinement applied to fluid dynamics and reactive media problems, (ESAIM: Proceedings, Vol. 34 (2011), EDP Sciences), 184-239 · Zbl 1302.65222
[8] Semplice, M.; Coco, A.; Russo, G., Adaptive mesh refinement for hyperbolic systems based on third-order compact WENO reconstruction, J. Sci. Comput., 66, 2, 692-724 (2016) · Zbl 1335.65077
[9] Dudley Ward, N.; Falle, S.; Olson, M. S., Modeling chemotactic waves in saturated porous media using adaptive mesh refinement, Transp. Porous Media, 89, 3, 487 (2011)
[10] Botti, L.; Piccinelli, M.; Ene-Iordache, B.; Remuzzi, A.; Antiga, L., An adaptive mesh refinement solver for large-scale simulation of biological flows, Int. J. Numer. Methods Biomed. Eng., 26, 1, 86-100 (2010) · Zbl 1180.92021
[11] Wise, S. M.; Lowengrub, J. S.; Frieboes, H. B.; Cristini, V., Three-dimensional multispecies nonlinear tumor growth - I: Model and numerical method, J. Theoret. Biol., 253, 3, 524-543 (2008) · Zbl 1398.92135
[12] Frieboes, H. B.; Zheng, X.; Sun, C. H.; Tromberg, B.; Gatenby, R.; Christini, V., An integrated computational/experimental model of tumor invasion, Cancer Res., 66, 3, 1597-1604 (2006)
[13] Kolbe, N.; Kat’uchová, J.; Sfakianakis, N.; Hellmann, N.; Lukáčová-Medvid’ová, M., A study on time discretization and adaptive mesh refinement methods for the simulation of cancer invasion : The urokinase model, Appl. Math. Comput., 273, 353-376 (2016) · Zbl 1410.92049
[14] Hanahan, D.; Weinberg, R. A., Hallmarks of cancer: the next generation, Cell, 144, 5, 646-674 (2011)
[15] MacKenzie, J.; Mewki, W. R., An unconditionally stable second-order accurate ALE-FEM scheme for two-dimensional convection-diffusion problems, IMA J. Numer. Anal., 32, 888-905 (2012) · Zbl 1267.65121
[16] Edelsbrunner, H., Geometry and Topology for Mesh Generation (2001), Cambridge University Press · Zbl 0981.65028
[17] Espejo, E.; Vilches, K.; Conca, C., A simultaneous blow-up problem arising in tumor modeling, J. Math. Biol., 79, 1357-1399 (2019) · Zbl 1423.35162
[18] Tao, Y.; Winkler, M., Critical mass for infinite-time blow-up in a haptotaxis system with nonlinear zero-order interaction, Discrete Contin. Dyn. Syst., 41, 1, 439-454 (2021) · Zbl 1458.35076
[19] Hellmann, N.; Kolbe, N.; Sfakianakis, N., A mathematical insight in the epithelial-mesenchymal-like transition in cancer cells and its effect in the invasion of the extracellular matrix, Bull. Braz. Math. Soc., 47, 1, 397-412 (2016) · Zbl 1359.92048
[20] Sfakianakis, N.; Kolbe, N.; Hellmann, N.; Lukáčová-Medvid’ová, M., A multiscale approach to the migration of cancer stem cells: Mathematical modelling and simulations, B. Math. Biol. (2016), in press
[21] Dobkin, D. P.; Laszlo, M. J., Primitives for the manipulation of three-dimensional subdivisions, Algorithmica, 4, 3-32 (1989) · Zbl 0664.68023
[22] Blandford, D. K.; Guy, E. B.; Cardoze, D. E.; Kadow, C., Compact representations of simplicial meshes in two and three dimensions, Int. J. Comput. Geom. AP, 15, 1, 3-24 (2005) · Zbl 1060.65555
[23] Alumbaugh, T. J.; Jiao, X., Compact array-based mesh data structures, (Hanks, Byron W., Proceedings of the 14th International Meshing Roundtable (2005), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 485-503
[24] Canino, D.; De Floriani, L.; Weiss, K., IA An adjacency-based representation for non-manifold simplicial shapes in arbitrary dimensions, Comput. Graph., 35, 3, 747-753 (2011)
[25] Dyedov, V.; Ray, N.; Einstein, D.; Jiao, X.; Tautges, T. J., AHF: array-based half-facet data structure for mixed-dimensional and non-manifold meshes, Eng. Comput., 31, 3, 389-404 (2015)
[26] R.S. Sampath, S.S. Adavani, H. Sundar, I. Lashuk, G. Biros, Dendro: Parallel algorithms for multigrid and AMR methods on 2:1 balanced octrees, in: SC ’08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, 2008, pp. 1-12.
[27] Kremer, M.; Bommes, D.; Kobbelt, L. P., Openvolumemesh - A versatile index-based data structure for 3D polytopal complexes, (IMR (2012))
[28] Kirk, B. S.; Peterson, J. W.; Stogner, R. H.; Carey, G. F., Libmesh : A C++ library for parallel adaptive mesh refinement/coarsening simulations, Eng. Comput., 22, 3, 237-254 (2006)
[29] Beall, M. W.; Shephard, M. S., A general topology-based mesh data structure, Internat. J. Numer. Methods Engrg., 40, 1573-1596 (1997)
[30] Carey, G. F.; Sharma, M.; Wang, K. C., A class of data structures for 2-d and 3-d adaptive mesh refinement, Internat. J. Numer. Methods Engrg., 26, 2607-2622 (1988) · Zbl 0673.73055
[31] Becker, E. B.; Carey, G. F.; Oden, J. T., (Finite Elements. Finite Elements, The Texas Finite Element Series (1981), Prentice-Hall: Prentice-Hall Englewood Cliffs, N.J) · Zbl 0459.65070
[32] Kremer, M.; Bommes, D.; Kobbelt, L., Openvolumemesh - A versatile index-based data structure for 3D polytopal complexes, (Jiao, X.; Weill, J.-Chr., Proceedings of the 21st International Meshing Roundtable (2013), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 531-548
[33] Fabri, A.; Giezeman, G. J.; Kettner, L.; Schirra, S., On the design of CGAL, A computational geometry algorithms library, Softw. - Pract. Exp., 30, 1167-1202 (2000) · Zbl 1147.68781
[34] Schroeder, W.; Martin, K.; Lorensen, B., The Visualization Toolkit: An Object-Oriented Approach To 3D Graphics ; Visualize Data in 3D - Medical, Engineering Or Scientific ; Build Your Own Applications with C++, Tcl, Java Or Python ; Includes Source Code for VTK (Supports Unix, Windows and Mac) (2006), Kitware, Inc: Kitware, Inc Clifton Park, NY
[35] Dyedov, V.; Einstein, D. R.; X., Jiao; Kuprat, A. P.; Carson, J. P.; del Pin, F., Variational generation of prismatic boundary-layer meshes for biomedical computing, Int. J. Numer. Methods Eng., 79, 907-945 (2009) · Zbl 1171.76440
[36] Burstedde, C.; Wilcox, L. C.; Ghattas, O., p4est: Scalable algorithms for parallel adaptive mesh refinement on forests of octrees, SIAM J. Sci. Comput., 33, 1103-1133 (2011) · Zbl 1230.65106
[37] Tobin, I.; Carsten, B.; Wilcox, L. C.; Ghattas, O., Recursive algorithms for distributed forests of octrees, SIAM J. Sci. Comput., 37, 5, C497-C531 (2015) · Zbl 1323.65105
[38] Bangerth, W.; Hartmann, R.; Kanschat, G., Deal.II—A general-purpose object-oriented finite element library, ACM Trans. Math. Software, 33, 4 (2007) · Zbl 1365.65248
[39] Bangerth, B.; Burstedde, C.; Heister, T.; Kronbichler, M., Algorithms and data structures for massively parallel generic adaptive finite element codes, ACM Trans. Math. Software, 38, 2, 14:1-14:28 (2011) · Zbl 1365.65247
[40] Alkämper, M.; Dedner, A.; Klöfkorn, R.; Nolte, M., The DUNE-ALUGrid module, Arch. Numer. Softw., 4, 1, 1-28 (2016)
[41] Bastian, P.; Blatt, M.; Dedner, A.; Dreier, N.-A.; Engwer, Chr.; Fritze, R.; Gräser, C.; Grüninger, Chr.; Kempf, D.; Klöfkorn, R.; Ohlberger, M.; Sander, O., The dune framework: Basic concepts and recent developments, Comput. Math. Appl., 81, 75-112 (2021) · Zbl 1524.65003
[42] Bastian, P.; Blatt, M.; Dedner, A.; Engwer, C.; Klöfkorn, R.; Ohlberger, M.; Sander, O., A generic grid interface for parallel and adaptive scientific computing. Part I: Abstract framework, Computing, 82, 103-119 (2008) · Zbl 1151.65089
[43] Bastian, P.; Blatt, M.; Dedner, A.; Engwer, C.; Klöfkorn, R.; Ohlberger, M.; Sander, O., A generic grid interface for parallel and adaptive scientific computing. Part II: Implementation and tests in DUNE, Computing, 82, 121-138 (2008) · Zbl 1151.65088
[44] Ma, G.; Kirb, J. T.; Shib, F., Numerical simulation of tsunami waves generated by deformable submarine landslides, Ocean Model, 69, 146-165 (2013)
[45] Dimonte, G.; Youngs, D. L.; Dimits, A.; Weber, S.; Marinak, M.; Wunsch, C.; Robinson, A.; Andrews, M. J.; Ramaprabhu, P.; Calder, A. C.; Fryxell, B.; Biello, J.; Dursi, L.; MacNeice, P.; K., Olson; Ricker, P.; Rosner, R.; Timmes, F.; Tufo, H.; Young, Y.-N.; Zingale, M., A comparative study of the turbulent Rayleigh-Taylor instability using high-resolution three-dimensional numerical simulations: The alpha-group collaboration, Phys. Fluids, 16, 5, 1668-1693 (2004) · Zbl 1186.76143
[46] Anderson, E.; Bai, Z.; Bischof, C.; Blackford, S.; Demmel, J.; Dongarra, J.; Du Croz, J.; Greenbaum, A.; Hammarling, S.; McKenney, A.; Sorensen, D., LAPACK Users’ Guide (1999), Society for Industrial and Applied Mathematics: Society for Industrial and Applied Mathematics Philadelphia, PA · Zbl 0934.65030
[47] Harten, A.; Engquist, B.; Osher, S.; Chakravarthy, S. R., Uniformly high order accurate essentially non-oscillatory schemes, III, (Upwind and High-Resolution Schemes (1987), Springer), 218-290
[48] Shu, C. W.; Osher, S., Efficient implementation of essentially non-oscillatory shock-capturing schemes, J. Comput. Phys., 77, 2, 439-471 (1988) · Zbl 0653.65072
[49] Giles, M., Non-Reflecting Boundary Conditions for the Euler Equations (1988), Computational Fluid Dynamics Laboratory, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology
[50] Steger, J. L.; Warming, R. F., Flux vector splitting of the inviscid gasdynamic equations with application to finite-difference methods, J. Comput. Phys., 40, 2, 263-293 (1981) · Zbl 0468.76066
[51] Toro, E., Riemann Solvers and Numerical Methods for Fluid Dynamics (2009), Springer · Zbl 1227.76006
[52] Feistauer, M.; Felcman, J.; Straškraba, I., Mathematical and Computational Methods for Compressible Flow (2003), Oxford University Press on Demand · Zbl 1028.76001
[53] Chaplain, M. A.J.; Lolas, G., Mathematical modelling of cancer cell invasion of tissue. The role of the urokinase plasminogen activation system, Math. Models Methods Appl. Sci., 15, 11, 1685-1734 (2005) · Zbl 1094.92039
[54] Bellomo, N.; Li, N. K.; Maini, P. K., The foundations of cancer modelling: selected topics, speculations,& perspectives, Math. Mod. Meth. Appl., 253, 593-646 (2008) · Zbl 1151.92014
[55] Arduino, A.; Preziosi, L., A multiphase model of tumour segregation in situ by a heterogeneous extracellular matrix, Int. J. Non-Linear Mech., 75, 22-30 (2015)
[56] Preziosi, L., Cancer Modelling and Simulation (2003), CRC Press · Zbl 1039.92022
[57] Stinner, Chr.; Surulescu, Chr.; Uatay, A., Global existence for a go-or-grow multiscale model for tumor invasion with therapy, Math. Models Methods Appl. Sci., 26, 11, 2163-2201 (2016) · Zbl 1348.35282
[58] Marciniak-Czochra, A.; Stiehl, T., Mathematical modelling of leukemogenesis and cancer stem cell dynamics, Math. Mod. Nat. Phen., 7, 166-202 (2012) · Zbl 1241.92045
[59] Johnston, M. D.; Maini, P. K.; Jonathan-Chapman, S.; Edwards, C. M.; Bodmer, W. F., On the proportion of cancer stem cells in a tumour, J. Theoret. Biol., 266, 4, 708-711 (2010) · Zbl 1407.92045
[60] Anderson, A. R.A.; Chaplain, M. A.J.; Newman, E. L.; Steele, R. J.C.; Thompson, A. M., Mathematical modelling of tumour invasion and metastasis, Comput. Math. Methods Med., 2, 2, 129-154 (2000) · Zbl 0947.92012
[61] Roussos, E. T.; Condeelis, J. S.; Patsialou, A., Chemotaxis in cancer, Nat. Rev. Cancer, 11, 8, 573-587 (2011)
[62] Rao, J. S., Molecular mechanisms of glioma invasiveness: the role of proteases, Nat. Rev. Cancer, 3, 7, 489-501 (2003)
[63] Eymard, R.; Gallouët, T.; Herbin, R., Finite volume methods, (Handbook of Numerical Analysis, Vol. 7 (2000), Elsevier), 713-1018 · Zbl 0981.65095
[64] Belmouhoub, R., Modélisation Tridimensionnelle de La Genèse Des Bassins Sédimentaires (1996), Ecole Nationale Supérieure des Mines de Paris, (Ph.D. thesis)
[65] Coudière, Y.; Villedieu, Ph., Convergence rate of a finite volume scheme for the linear convection-diffusion equation on locally refined meshes, ESAIM Math. Model. Numer. Anal., 34, 6, 1123-1149 (2000) · Zbl 0972.65081
[66] Japanese Gastric Cancer Association, Japanese classification of gastric carcinoma: 3rd english edition, Gastric Cancer, 14, 2, 101-112 (2011)
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