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Informed Machine Learning

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 combina...

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Bibliographic Details
Main Authors:	Schulz, Daniel, Bauckhage, Christian
Format:	Book
Language:	English
Published:	Springer 2025
Subjects:	Informed Machine Learning Deep Learning Open Access
Online Access:	https://link.springer.com/book/10.1007/978-3-031-83097-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/11838
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spelling	oai:localhost:PNK-118382025-04-27T03:10:59Z Informed Machine Learning Schulz, Daniel Bauckhage, Christian Informed Machine Learning Deep Learning Open Access 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 applications where 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. 2025-04-27T03:10:59Z 2025-04-27T03:10:59Z 2025 Book https://link.springer.com/book/10.1007/978-3-031-83097-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/11838 en application/pdf Springer
institution	Digital Phenikaa
collection	Digital Phenikaa
language	English
topic	Informed Machine Learning Deep Learning Open Access
spellingShingle	Informed Machine Learning Deep Learning Open Access Schulz, Daniel Bauckhage, Christian Informed Machine Learning
description	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 applications where 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.
format	Book
author	Schulz, Daniel Bauckhage, Christian
author_facet	Schulz, Daniel Bauckhage, Christian
author_sort	Schulz, Daniel
title	Informed Machine Learning
title_short	Informed Machine Learning
title_full	Informed Machine Learning
title_fullStr	Informed Machine Learning
title_full_unstemmed	Informed Machine Learning
title_sort	informed machine learning
publisher	Springer
publishDate	2025
url	https://link.springer.com/book/10.1007/978-3-031-83097-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/11838
_version_	1830585004592201728
score	8.893527

Informed Machine Learning

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