{"product_id":"9783030924973","title":"Mathematical Geosciences: Hybrid Symbolic-Numeric Methods","description":"\u003ch1\u003eMathematical Geosciences: Hybrid Symbolic-Numeric Methods\u003c\/h1\u003e \u003ch2\u003eAwange, Joseph L.; Paláncz, Béla; Lewis, Robert H.; Völgyesi, Lajos\u003c\/h2\u003e \u003cp\u003eThis 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 illustratedby an image classification via  a regression problem.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2024-04-09\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783030924973\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-030-92495-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 715\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":46548031406220,"sku":"9783030924973","price":98.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783030924973.jpg?v=1776054931","url":"https:\/\/lateknightbooks.com\/products\/9783030924973","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}