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Book synopsis

The digital twin concept stands as a pivotal facilitator in the ongoing Industry 4.0 revolution, with one of its most significant advantages lying in its capacity to offer precise predictions. Specifically focusing on predictive capabilities, digital twins designated for this purpose are commonly known as predictive digital twins. Despite the growing recognition of their importance, discussions surrounding predictive digital twins remain fragmented and lacking a comprehensive resource. This gap becomes particularly pronounced in the academic world, where, as a university professor teaching a master's program course in digital twins, there arises a pressing need for a dedicated reference book to furnish students with a structured and in-depth exploration of predictive digital twins. Predictive Digital Twins: Foundations and Applications serves as a comprehensive guide that addresses the theoretical foundations, practical applications, and emerging trends associated with predictive digital twins. By doing so, the book fills the existing void in literature and academia, providing students, researchers, and practitioners with a valuable resource to enhance their understanding of this cutting-edge concept.

• Clear explanations and theories underpinning predictive digital twins to facilitate effective teaching and research.
• Real-world applications and success stories illustrating the practical implementation of predictive digital twins across different industries.
• Practical insights into implementing predictive digital twins for improving processes, especially in predictive maintenance.
• Guidance on best practices for developing, managing, and optimizing predictive digital twin systems.

Book details

Book category Books in English Technology, engineering, agriculture Electronics & communications engineering Electronics engineering

• Full title: Predictive Digital Twins
• Subtitle: Foundations and Applications
• Author: Agus Hasan
• Language: English
• Binding: Paperback
• Number of pages: 350
• EAN: 9780443274572
• ISBN: 0443274576
• ID: 50042207
• Publisher: Elsevier Science
• Weight: 450 g
• Dimensions: 229 × 152 mm
• Date of publishing: 01. July 2026

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