03427nam 22006015 450 991052008520332120251113190100.0981-16-5576-610.1007/978-981-16-5576-0(MiAaPQ)EBC6838602(Au-PeEL)EBL6838602(CKB)20275118700041(OCoLC)1290841485(PPN)259387517(DE-He213)978-981-16-5576-0(EXLCZ)992027511870004120211217d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierProceedings of the Forum "Math-for-Industry" 2018 Big Data Analysis, AI, Fintech, Math in Finances and Economics /edited by Jin Cheng, Xu Dinghua, Osamu Saeki, Tomoyuki Shirai1st ed. 2021.Singapore :Springer Nature Singapore :Imprint: Springer,2021.1 online resource (191 pages)Mathematics for Industry,2198-3518 ;35Print version: Cheng, Jin Proceedings of the Forum Math-For-Industry 2018 Singapore : Springer Singapore Pte. Limited,c2021 9789811655753 Includes bibliographical references.A 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.This 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.Mathematics for Industry,2198-3518 ;35Engineering mathematicsQuantitative researchStatisticsEngineering MathematicsData Analysis and Big DataApplied StatisticsEngineering mathematics.Quantitative research.Statistics.Engineering Mathematics.Data Analysis and Big Data.Applied Statistics.510.243631Cheng Jin1963-MiAaPQMiAaPQMiAaPQBOOK9910520085203321Proceedings of the Forum "Math-for-Industry" 20182910299UNINA