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035   $a(DE-He213)978-981-16-5576-0
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100   $a20211217d2021      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aProceedings of the Forum "Math-for-Industry" 2018 $eBig Data Analysis, AI, Fintech, Math in Finances and Economics /$fedited by Jin Cheng, Xu Dinghua, Osamu Saeki, Tomoyuki Shirai
205   $a1st ed. 2021.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021.
215   $a1 online resource (191 pages)
225 1 $aMathematics for Industry,$x2198-3518 ;$v35
311 08$aPrint version: Cheng, Jin Proceedings of the Forum Math-For-Industry 2018 Singapore : Springer Singapore Pte. Limited,c2021 9789811655753 
320   $aIncludes bibliographical references.
327   $aA Brief Review of Some Swarming Models using Stochastic Differential Equations -- Copula-based estimation of Value at Risk for the portfolio problem -- An Overview of Exact Solution Methods for Guaranteed Minimum Death Benefit Options in Variable Annuities -- Determinantal reinforcement learning with techniques to avoid poor local optima -- Surface Denoising based on Normal Filtering in a Robust Statistics Framework -- Mathematical Modeling and Inverse Problem Approaches for Functional -- Clothing Design based on Thermal Mechanism -- Unique continuation on a sphere for Helmholtz equation and its numerical treatments -- Notes on Backward Stochastic Differential Equations for Computing XVA.
330   $aThis volume includes selected technical papers presented at the Forum ?Math-for-Industry? 2018. The papers written by eminent researchers and academics working in the area of industrial mathematics from the viewpoint of financial mathematics, machine learning, neural networks, inverse problems, stochastic modelling, etc., discuss how the ingenuity of science, technology, engineering and mathematics are and will be expected to be utilized. This volume focuses on the role that mathematics-for-industry can play in interdisciplinary research to develop new methods. The contents are useful for researchers both in academia and industry working in interdisciplinary sectors.
410  0$aMathematics for Industry,$x2198-3518 ;$v35
606   $aEngineering mathematics
606   $aQuantitative research
606   $aStatistics
606   $aEngineering Mathematics
606   $aData Analysis and Big Data
606   $aApplied Statistics
615  0$aEngineering mathematics.
615  0$aQuantitative research.
615  0$aStatistics.
615 14$aEngineering Mathematics.
615 24$aData Analysis and Big Data.
615 24$aApplied Statistics.
676   $a510.243631
702   $aCheng$b Jin$f1963-
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910520085203321
996   $aProceedings of the Forum "Math-for-Industry" 2018$92910299
997   $aUNINA