Novel Deep Learning Methodologies in Industrial and Applied Mathematics

Buch | Hardcover

121 Seiten

2026
Springer Verlag, Singapore
978-981-95-0350-6 (ISBN)

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mso-fareast-font-family: 'Times New Roman';">This book presents a collection of research papers exploring innovative applications of Artificial Intelligence (AI) in Industrial and Applied Mathematics (IAM). Another contribution discusses eXplainable AI (XAI), highlighting how Clifford geometric algebra can enhance AI interpretability.

This book presents a collection of research papers exploring innovative applications of Artificial Intelligence (AI) in Industrial and Applied Mathematics (IAM). It begins with an introduction to the knowlEdge Platform, a software solution for managing the AI lifecycle in Industry 5.0, integrating AI, IoT, and edge computing to support human-AI collaboration across cloud-to-edge systems. The next chapter offers an accessible overview of geometric deep learning, focusing on geometric algebra transformers and their applications. Another contribution discusses eXplainable AI (XAI), highlighting how Clifford geometric algebra can enhance AI interpretability. Further, an improved Quaternion Monogenic Convolutional Neural Network Layer (QMCL) is presented, demonstrating resilience to brightness changes and adversarial attacks. The book also addresses the challenge of balancing computational efficiency, privacy, and accuracy in distributed AI, proposing model partitioning and early exit strategies. A data-driven method for fault prognosis in wind turbine main bearings is introduced, using industrial-scale turbine data. Finally, recent publications—particularly those following the International Congress of Industrial and Applied Mathematics 2023—are reviewed, offering insights into emerging research directions in AI and IAM.

Sebastian Xambó-Descamps, Ed. is a Emeritus Full Professor of Mathematics at the Universitat Politècnica de Catalunya / Facultat de Matemàtiques i Estadística (UPC/FME).

Chapter 1 Automating AI Lifecycle Management in Industry 5.0.- Chapter 2 Geometric Algebra Transformers: Revolutionizing Geometric Data.- Chapter 3 Innovative Models based on Geometric Calculus for Explainable Artificial Intelligence.- Chapter 4 Robust Image Classification with Quaternion Monogenic Signal ConvNet under Brightness Changes and Adversarial Attacks.- Chapter 5 Optimization Approaches for Distributed AI Models on Edge Devices.- Chapter 6 Artificial Intelligence for Wind Turbine Predictive Maintenance.- Chapter 7 Epilog.

Erscheinungsdatum	28.11.2025
Reihe/Serie	ICIAM2023 Springer Series
Zusatzinfo	50 Illustrations, color; 2 Illustrations, black and white
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Mathematik / Informatik ► Mathematik ► Analysis
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
Schlagworte	adversarial attacks • Articicial intelligence • Clifford geometric algebra • Cloud-to-edge cotinuum • data privacy • Distributed edge-computing • Explainable Artificial Intelligence • Geometric algebra transformers • Geometric Deep Learning • internet of things • Lifecycle management in Industry 5.0 • Model partitioning and model early exit • Quoternion monogenic convolutional neural network • Research opportunities • Wind turbines' main bearing fault prognosis
ISBN-10	981-95-0350-7 / 9819503507
ISBN-13	978-981-95-0350-6 / 9789819503506
Zustand	Neuware