TY - JOUR
T1 - Partial tensor decomposition for decoupling isogeometric Galerkin discretizations
AU - Scholz, Felix
AU - Mantzaflaris, Angelos
AU - Jüttler, Bert
N1 - Funding Information:
The authors gratefully acknowledge the support provided by the Austrian Science Fund (FWF) through project NFN S11708 and by the European Research Council (ERC) , project GA 694515 .
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - System matrix assembly for isogeometric (i.e., spline-based) discretizations of partial differential equations is more challenging than for classical finite elements, due to the increased polynomial degrees and the larger (and hence more overlapping) supports of the basis functions. The global tensor-product structure of the discrete spaces employed in isogeometric analysis can be exploited to accelerate the computations, using sum factorization, precomputed look-up tables, and tensor decomposition. We generalize the third approach by considering partial tensor decompositions. We show that the resulting new method preserves the global discretization error and that its computational complexity compares favorably to the existing approaches. Moreover, the numerical realization simplifies considerably since it relies on standard techniques from numerical linear algebra.
AB - System matrix assembly for isogeometric (i.e., spline-based) discretizations of partial differential equations is more challenging than for classical finite elements, due to the increased polynomial degrees and the larger (and hence more overlapping) supports of the basis functions. The global tensor-product structure of the discrete spaces employed in isogeometric analysis can be exploited to accelerate the computations, using sum factorization, precomputed look-up tables, and tensor decomposition. We generalize the third approach by considering partial tensor decompositions. We show that the resulting new method preserves the global discretization error and that its computational complexity compares favorably to the existing approaches. Moreover, the numerical realization simplifies considerably since it relies on standard techniques from numerical linear algebra.
KW - Isogeometric analysis
KW - Low-rank approximation
KW - Matrix assembly
KW - Numerical integration
KW - Singular value decomposition
KW - Tensor decomposition
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U2 - 10.1016/j.cma.2018.03.026
DO - 10.1016/j.cma.2018.03.026
M3 - Article
AN - SCOPUS:85044789086
VL - 336
SP - 485
EP - 506
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0374-2830
ER -