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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Informed Machine Learning (Cognitive Technologies)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Domain-informed Machine Learning for Smart Manufacturing
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Domain-Informed Machine Learning for Smart Manufacturing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Add to basketHardcover. Condition: Brand New. 352 pages. 9.25x6.10x9.21 inches. In Stock.
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Domain-Informed Machine Learning for Smart Manufacturing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Add to basketHardcover. Condition: Brand New. 450 pages. 9.25x6.10x9.49 inches. In Stock.
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Domain-informed Machine Learning for Smart Manufacturing
Language: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031916301 ISBN 13: 9783031916304
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New.
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Machine Learning for Engineers : Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
Language: English
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2024
ISBN 10: 366269994X ISBN 13: 9783662699942
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.
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Informed Machine Learning
Language: English
Published by Springer Nature Switzerland, Springer Nature Switzerland, 2025
ISBN 10: 3031830962 ISBN 13: 9783031830969
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applicationswhere the aspect of small data sets is a challenge.Machine Learning with small amounts of data After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of 'Informed Machine Learning' comes into play.Informed Machine Learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable.
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Taschenbuch. Condition: Neu. Machine Learning for Engineers | Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications | Marcus J. Neuer | Taschenbuch | xvii | Englisch | 2024 | Springer | EAN 9783662699942 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Domain-informed Machine Learning for Smart Manufacturing
Language: English
Published by Springer Verlag GmbH, 2025
ISBN 10: 3031916301 ISBN 13: 9783031916304
Gebunden. Condition: New.
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International EditionUS$ 174.32
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Add to basketHardcover. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Condition: As New. Unread book in perfect condition.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces the state-of-the-art understanding on domain-informed machine learning (DIML) for advanced manufacturing. Methods and case studies presented in this volume show how complicated engineering phenomena and mechanisms are integrated into machine learning problem formulation and methodology development. Ultimately, these methodologies contribute to quality control for smart personalized manufacturing. The topics include domain-informed feature representation, dimension reduction for personalized manufacturing, fabrication-aware modeling of additive manufacturing processes, small-sample machine learning for 3D printing quality, optimal compensation of 3D shape deviation in 3D printing, engineering-informed transfer learning for smart manufacturing, and domain-informed predictive modeling for nanomanufacturing quality. Demonstrating systematically how the various aspects of domain-informed machine learning methods are developed for advanced manufacturing such as additive manufacturing and nanomanufacturing, the book is ideal for researchers, professionals, and students in manufacturing and related engineering fields.
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US$ 189.01
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Domain-informed Machine Learning for Smart Manufacturing
Language: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031916301 ISBN 13: 9783031916304
US$ 122.13
US$ 86.91 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardback. Condition: New.
