Proceedings of the Forum "Math-for-Industry" 2018

Big Data Analysis, AI, Fintech, Math in Finances and Economics
Herausgegeben:Cheng, Jin; Dinghua, Xu; Saeki, Osamu; Shirai, Tomoyuki

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Mathematics for Industry 35
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-16-5575-3
• 1st ed. 2021
• Seitenzahl: 196
• Erscheinungstermin: 18. Dezember 2021
• Englisch
• Abmessung: 241mm x 160mm x 17mm
• Gewicht: 465g
• ISBN-13: 9789811655753
• ISBN-10: 9811655758
• Artikelnr.: 62266358

Autorenporträt

Jin Cheng is a professor at Fudan University, China. He pursued his Ph.D. from the same university. He is currently President of Shanghai Society of Industrial and Applied Mathematics and Director of Shanghai Key Laboratory of Contemporary Applied Mathematics in Fudan University. His research is funded by National Science Foundation of China, the Ministry of Science and Technology of China and Shanghai Municipal Government. Dr Cheng is also on the editorial board of several scientific journals. In 1999, he received the ISACC Young Scientist Award from the International Society for Analysis, its Applications and Computation (ISAAC) in Berlin, Germany. His current research interests concern the inverse problems for partial differential equations, mathematical modelling and analysis, regularization methods for ill-posed problems.  Osamu Saeki is a distinguished professor at Kyushu University. He received his Ph.D. in Mathematics from the University of Tokyo. He was awarded the Takebe Katahiro Prize 1996 and the Geometry Prize 2015 from the Mathematical Society of Japan. He was involved in establishing the Institute of Mathematics for Industry (IMI), Kyushu University, launched in 2011. He is also engaged in the education of industrial mathematics and is the coordinator of the WISE program "Graduate Program of Mathematics for Innovation", supported by MEXT, Japan. His current research interests concern topology, singularity theory, topology of low-dimensional manifolds, knot theory and visualization of large scale data.  Dinghua Xu is a Professor at Zhejiang Sci-Tech University (ZSTU) and Shanghai University of Finance and Economics (SHUFE). He pursued his Ph.D. degree in Computational Mathematics from Shanghai University, China. He has been awarded the National Distinguished Teacher of China in 2004 and the National Excellent Teaching Achievement Prize in 2014 from the Ministry of Education, China. He was instrumental inthe establishment of the School of Mathematics and Informational Sciences (SMIS), ECIT, launched in 2003. His current research interest covers  computable modeling, inverse problems for parabolic equations, data modelling for textile material design and for multiscale modeling in catalyst preparation process, numerical computation for partial differential equations.  Tomoyuki Shirai received his Ph.D. in Mathematical Sciences at the University of Tokyo in 1996 by a thesis on spectral analysis on discrete Schrödinger operators. He joined the Faculty of Mathematics, Kyushu University in 2004 as an associate professor and became a full professor in 2009. He has been a full professor at the Institute of Mathematics for Industry (IMI), Kyushu University, which was launched in 2011. His research interests are in probability theory, stochastic processes and their applications including the probabilistic aspect of topological data analysis. He is the principal investigator of two Grant-in-Aid for Scientific Research under by JSPS on probability theory and related fields and a co-investigator of CREST project "Topological data analysis for new descriptors on soft matters".

Inhaltsangabe

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.