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Artificial intelligence for scientif...

Iten, Raban.

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Artificial intelligence for scientific discoveries = extracting physical concepts from experimental data using deep learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for scientific discoveries/ by Raban Iten.
其他題名:	extracting physical concepts from experimental data using deep learning /
作者:	Iten, Raban.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiii, 170 p. :ill., digital ;24 cm.
內容註:	Introduction -- Machine Learning Background -- Overview of Using Machine Learning for Physical Discoveries -- Theory: Formalizing the Process of Human Model Building -- Methods: Using Neural Networks to Find Simple Representations -- Applications: Physical Toy Examples -- Open Questions and Future Prospects.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-031-27019-2
ISBN:	9783031270192

Artificial intelligence for scientific discoveries = extracting physical concepts from experimental data using deep learning /
Iten, Raban.

Artificial intelligence for scientific discoveriesextracting physical concepts from experimental data using deep learning /[electronic resource] :by Raban Iten. - Cham :Springer International Publishing :2023. - xiii, 170 p. :ill., digital ;24 cm.

Introduction -- Machine Learning Background -- Overview of Using Machine Learning for Physical Discoveries -- Theory: Formalizing the Process of Human Model Building -- Methods: Using Neural Networks to Find Simple Representations -- Applications: Physical Toy Examples -- Open Questions and Future Prospects.

Will research soon be done by artificial intelligence, thereby making human researchers superfluous? This book explains modern approaches to discovering physical concepts with machine learning and elucidates their strengths and limitations. The automation of the creation of experimental setups and physical models, as well as model testing are discussed. The focus of the book is the automation of an important step of the model creation, namely finding a minimal number of natural parameters that contain sufficient information to make predictions about the considered system. The basic idea of this approach is to employ a deep learning architecture, SciNet, to model a simplified version of a physicist's reasoning process. SciNet finds the relevant physical parameters, like the mass of a particle, from experimental data and makes predictions based on the parameters found. The author demonstrates how to extract conceptual information from such parameters, e.g., Copernicus' conclusion that the solar system is heliocentric.

ISBN: 9783031270192

Standard No.: 10.1007/978-3-031-27019-2doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q180.55.D57

Dewey Class. No.: 507.2

Artificial intelligence for scientific discoveries = extracting physical concepts from experimental data using deep learning /
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