Springer Proceedings of the Forum "Math-for-Industry" 2018: Big Data Analysis, AI, Fintech, Math in Finances and Economics: 35 (Mathematics for Industry, 35)

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. From the Back Cover 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. About the Author 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 in the 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 assoc