Mathematics for Industry: Big Data Analysis, AI, Fintech, Math in Finances and Economics

Cheng, Jin; Dinghua, Xu; Saeki, Osamu; Shirai, Tomoyuki

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.

Details

Published by: Springer

Publication Date: 2022-12-19

Format: Paperback

ISBN-13: 9789811655784

DOI: 10.1007/978-981-16-5576-0

Dimensions: 235cm x155cm

Pages: 179