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020 _a9783031024115
_9978-3-031-02411-5
024 7 _a10.1007/978-3-031-02411-5
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
072 7 _aPB
_2thema
082 0 4 _a510
_223
100 1 _aSalehi, Younes.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981234
245 1 0 _aNumerical Integration of Space Fractional Partial Differential Equations
_h[electronic resource] :
_bVol 1 - Introduction to Algorithms and Computer Coding in R /
_cby Younes Salehi, William E. Schiesser.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXII, 188 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Mathematics & Statistics,
_x1938-1751
505 0 _aPreface -- Introduction to Fractional Partial Differential Equations -- Variation in the Order of the Fractional Derivatives -- Dirichlet, Neumann, Robin BCs -- Convection SFPDEs -- Nonlinear SFPDEs -- Authors' Biographies -- Index.
520 _aPartial differential equations (PDEs) are one of the most used widely forms of mathematics in science and engineering. PDEs can have partial derivatives with respect to (1) an initial value variable, typically time, and (2) boundary value variables, typically spatial variables. Therefore, two fractional PDEs can be considered, (1) fractional in time (TFPDEs), and (2) fractional in space (SFPDEs). The two volumes are directed to the development and use of SFPDEs, with the discussion divided as: Vol 1: Introduction to Algorithms and Computer Coding in R Vol 2: Applications from Classical Integer PDEs. Various definitions of space fractional derivatives have been proposed. We focus on the Caputo derivative, with occasional reference to the Riemann-Liouville derivative. The Caputo derivative is defined as a convolution integral. Thus, rather than being local (with a value at a particular point in space), the Caputo derivative is non-local (it is based on an integration in space), which is one of the reasons that it has properties not shared by integer derivatives. A principal objective of the two volumes is to provide the reader with a set of documented R routines that are discussed in detail, and can be downloaded and executed without having to first study the details of the relevant numerical analysis and then code a set of routines. In the first volume, the emphasis is on basic concepts of SFPDEs and the associated numerical algorithms. The presentation is not as formal mathematics, e.g., theorems and proofs. Rather, the presentation is by examples of SFPDEs, including a detailed discussion of the algorithms for computing numerical solutions to SFPDEs and a detailed explanation of the associated source code.
650 0 _aMathematics.
_911584
650 0 _aStatisticsĀ .
_931616
650 0 _aEngineering mathematics.
_93254
650 1 4 _aMathematics.
_911584
650 2 4 _aStatistics.
_914134
650 2 4 _aEngineering Mathematics.
_93254
700 1 _aSchiesser, William E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981235
710 2 _aSpringerLink (Online service)
_981236
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002571
776 0 8 _iPrinted edition:
_z9783031012839
776 0 8 _iPrinted edition:
_z9783031035395
830 0 _aSynthesis Lectures on Mathematics & Statistics,
_x1938-1751
_981237
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02411-5
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942 _cEBK
999 _c85135
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