國立高雄大學圖資館 |

Language: English

Back

Mathematical geoscienceshybrid symbolic-numeric methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical geosciencesby Joseph L. Awange ... [et al.].
Reminder of title:	hybrid symbolic-numeric methods /
other author:	Awange, Joseph L.
Published:	Cham :Springer International Publishing :2023.
Description:	xxix, 715 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Earth sciencesMathematics.
Online resource:	https://doi.org/10.1007/978-3-030-92495-9
ISBN:	9783030924959$q(electronic bk.)

Mathematical geoscienceshybrid symbolic-numeric methods /
Mathematical geoscienceshybrid symbolic-numeric methods /[electronic resource] :by Joseph L. Awange ... [et al.]. - Second edition. - Cham :Springer International Publishing :2023. - xxix, 715 p. :ill., digital ;24 cm.

Introduction -- Solution of nonlinear systems -- Solution of algebraic polynomial systems -- Homotopy solution of nonlinear systems -- Over and underdeterminated systems -- Nonlinear geodetic equations with uncertainties -- Optimization of systems -- Simulated annealing.

This second edition of Mathematical Geosciences book adds five new topics: Solution equations with uncertainty, which proposes two novel methods for solving nonlinear geodetic equations as stochastic variables when the parameters of these equations have uncertainty characterized by probability distribution. The first method, an algebraic technique, partly employs symbolic computations and is applicable to polynomial systems having different uncertainty distributions of the parameters. The second method, a numerical technique, uses stochastic differential equation in Ito form; Nature Inspired Global Optimization where Meta-heuristic algorithms are based on natural phenomenon such as Particle Swarm Optimization. This approach simulates, e.g., schools of fish or flocks of birds, and is extended through discussion of geodetic applications. Black Hole Algorithm, which is based on the black hole phenomena is added and a new variant of the algorithm code is introduced and illustrated based on examples; The application of the Gröbner Basis to integer programming based on numeric symbolic computation is introduced and illustrated by solving some standard problems; An extension of the applications of integer programming solving phase ambiguity in Global Navigation Satellite Systems (GNSSs) is considered as a global quadratic mixed integer programming task, which can be transformed into a pure integer problem with a given digit of accuracy. Three alternative algorithms are suggested, two of which are based on local and global linearization via McCormic Envelopes; and Machine learning techniques (MLT) that offer effective tools for stochastic process modelling. The Stochastic Modelling section is extended by the stochastic modelling via MLT and their effectiveness is compared with that of the modelling via stochastic differential equations (SDE) Mixing MLT with SDE also known as frequently Neural Differential Equations is also introduced and illustrated by an image classification via a regression problem.

ISBN: 9783030924959$q(electronic bk.)

Standard No.: 10.1007/978-3-030-92495-9doiSubjects--Topical Terms:

276480
Earth sciences
--Mathematics.

LC Class. No.: QE33.2.M3

Dewey Class. No.: 550.151

Mathematical geoscienceshybrid symbolic-numeric methods /
LDR:03328nmm a2200337 a 4500 001 643357
003 DE-He213
005 20230407010555.0
006 m d
007 cr nn 008maaau
008 231123s2023 sz s 0 eng d
020 $a 9783030924959$q(electronic bk.)
020 $a 9783030924942$q(paper)
024 7 $a 10.1007/978-3-030-92495-9 $2 doi
035 $a 978-3-030-92495-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QE33.2.M3
072 7 $a RB $2 bicssc
072 7 $a SCI019000 $2 bisacsh
072 7 $a RB $2 thema
082 0 4 $a 550.151 $2 23
090 $a QE33.2.M3 $b M426 2023
245 0 0 $a Mathematical geosciences $h [electronic resource] : $b hybrid symbolic-numeric methods / $c by Joseph L. Awange ... [et al.].
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxix, 715 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Solution of nonlinear systems -- Solution of algebraic polynomial systems -- Homotopy solution of nonlinear systems -- Over and underdeterminated systems -- Nonlinear geodetic equations with uncertainties -- Optimization of systems -- Simulated annealing.
520 $a This second edition of Mathematical Geosciences book adds five new topics: Solution equations with uncertainty, which proposes two novel methods for solving nonlinear geodetic equations as stochastic variables when the parameters of these equations have uncertainty characterized by probability distribution. The first method, an algebraic technique, partly employs symbolic computations and is applicable to polynomial systems having different uncertainty distributions of the parameters. The second method, a numerical technique, uses stochastic differential equation in Ito form; Nature Inspired Global Optimization where Meta-heuristic algorithms are based on natural phenomenon such as Particle Swarm Optimization. This approach simulates, e.g., schools of fish or flocks of birds, and is extended through discussion of geodetic applications. Black Hole Algorithm, which is based on the black hole phenomena is added and a new variant of the algorithm code is introduced and illustrated based on examples; The application of the Gröbner Basis to integer programming based on numeric symbolic computation is introduced and illustrated by solving some standard problems; An extension of the applications of integer programming solving phase ambiguity in Global Navigation Satellite Systems (GNSSs) is considered as a global quadratic mixed integer programming task, which can be transformed into a pure integer problem with a given digit of accuracy. Three alternative algorithms are suggested, two of which are based on local and global linearization via McCormic Envelopes; and Machine learning techniques (MLT) that offer effective tools for stochastic process modelling. The Stochastic Modelling section is extended by the stochastic modelling via MLT and their effectiveness is compared with that of the modelling via stochastic differential equations (SDE) Mixing MLT with SDE also known as frequently Neural Differential Equations is also introduced and illustrated by an image classification via a regression problem.
650 0 $a Earth sciences $x Mathematics. $3 276480
650 1 4 $a Earth Sciences. $3 309702
650 2 4 $a Mathematical Applications in Environmental Science. $3 913336
650 2 4 $a Mathematics of Planet Earth. $3 779959
650 2 4 $a Mathematical Methods in Physics. $3 273796
650 2 4 $a Earth System Sciences. $3 559038
650 2 4 $a Geophysics. $3 197003
700 1 $a Awange, Joseph L. $3 485080
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-92495-9
950 $a Mathematics and Statistics (SpringerNature-11649)