東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Emerging technologies in computational sciences for industry, sustainability and innovation = math to product /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Emerging technologies in computational sciences for industry, sustainability and innovation/ edited by Matteo Giacomini, Simona Perotto, Gianluigi Rozza.
Reminder of title:	math to product /
other author:	Giacomini, Matteo.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	ix, 382 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Industrial engineering - Congresses. - Mathematics -
Online resource:	https://doi.org/10.1007/978-3-031-95709-3
ISBN:	9783031957093

Emerging technologies in computational sciences for industry, sustainability and innovation = math to product /
Emerging technologies in computational sciences for industry, sustainability and innovationmath to product /[electronic resource] :edited by Matteo Giacomini, Simona Perotto, Gianluigi Rozza. - Cham :Springer Nature Switzerland :2025. - ix, 382 p. :ill. (chiefly col.), digital ;24 cm. - Lecture notes in computational science and engineering,v. 1462197-7100 ;. - Lecture notes in computational science and engineering ;v. 146..

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.

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.

ISBN: 9783031957093

Standard No.: 10.1007/978-3-031-95709-3doiSubjects--Topical Terms:

3226305
Industrial engineering
--Mathematics--Congresses.

LC Class. No.: T57

Dewey Class. No.: 620.00151

Emerging technologies in computational sciences for industry, sustainability and innovation = math to product /
LDR:03558nmm a22003375a 4500 001 2422382
003 DE-He213
005 20251123125533.0
006 m d
007 cr nn 008maaau
008 260505s2025 sz s 0 eng d
020 $a 9783031957093 $q (electronic bk.)
020 $a 9783031957086 $q (paper)
024 7 $a 10.1007/978-3-031-95709-3 $2 doi
035 $a 978-3-031-95709-3
040 $a GP $c GP
041 0 $a eng
050 4 $a T57
072 7 $a PBKS $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 620.00151 $2 23
090 $a T57 $b .E53 2025
245 0 0 $a Emerging technologies in computational sciences for industry, sustainability and innovation $h [electronic resource] : $b math to product / $c edited by Matteo Giacomini, Simona Perotto, Gianluigi Rozza.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a ix, 382 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computational science and engineering, $x 2197-7100 ; $v v. 146
505 0 $a 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.
520 $a 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.
650 0 $a Industrial engineering $x Mathematics $v Congresses. $3 3226305
650 1 4 $a Computational Science and Engineering. $3 893018
650 2 4 $a Mathematics of Computing. $3 891213
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Giacomini, Matteo. $3 3784325
700 1 $a Perotto, Simona. $3 2145779
700 1 $a Rozza, Gianluigi. $3 1621072
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a Math to Product (Conference) $n (1st : $d 2023 : $c Taormina, Italy) $3 3804147
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computational science and engineering ; $v v. 146. $3 3804146
856 4 0 $u https://doi.org/10.1007/978-3-031-95709-3
950 $a Mathematics and Statistics (SpringerNature-11649)