Emerging Technologies in Computational Sciences for Industry, Sustainability and Innovation
Math to Product
Series: Lecture Notes in Computational Science and Engineering; 146;
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 31 December 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031957086
- Binding Hardback
- No. of pages382 pages
- Size 235x155 mm
- Language English
- Illustrations IX, 382 p. 159 illus., 149 illus. in color. Illustrations, black & white 700
Categories
Long description:
This book presents up-to-date state of the art for industrial mathematics and covers emerging topics in computational sciences. Mathematical models and computational methods have gained an increasing importance in the simulation of real-world and industrial problems. The employment of such methodologies deeply changed the standard ways of conceiving daily industrial production and strategies for sustainable exploitation of modern cities. The goal pursued by this book is twofold. On the one hand, cases of successful interaction between mathematics and industry are presented. Special emphasis is devoted to the benefits provided by the transfer of knowledge in different fields of applications, including the social challenges of sustainable development. On the other hand, groundbreaking ideas and emerging technologies in computational science are discussed to foster cross-fertilization of academic solutions and real-world problems. Math to Product (M2P) is meant to establish a platform for proposal, discussion, and promotion of current and new trends in industry, sustainability, and innovation, with the goal of supporting creative and interdisciplinary thoughts. Scientific/technical areas covered include transfer of knowledge, innovation in design, computational science and engineering, industrial optimization processes, sustainable mobility, aerospace, automotive, nautical and naval engineering.
Target audience is made up by grad students and researchers in the field.
Table of Contents:
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Reduced Order Modeling in computational fluid dynamics: an overview of methods and applications.- Digital Twins for Predictive Maintenance in Industry: A Statistical and Deep Learning-Based Approach.- Hybrid energy system based on constrained optimization using simulated annealing.- Investigating ANN accuracy changes through cluster-based cost function modification.- On the use of manifold learning tools for coherent object interpolation based on geometrical and topological descriptors.- Design of a checkerboard counter flow heat exchanger for industrial applications.- A PINN framework for perturbed poromechanical models.- Exploiting scientific machine learning on embedded digital twins.- Review of: Simulations of thermally-driven winds on Mars:the Gale crater case.- Industrial applications of lift and drag forces in chaotic flow.- T8code- Scalable Adaptive Mesh Refinement.
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