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1. |
Record Nr. |
UNINA9910698653503321 |
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Autore |
Iten Raban |
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Titolo |
Artificial Intelligence for Scientific Discoveries : Extracting Physical Concepts from Experimental Data Using Deep Learning / / Raban Iten |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2023] |
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©2023 |
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ISBN |
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3-031-27019-3 |
9783031270192 |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource : illustrations |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Discoveries in science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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system is heliocentric. . |
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2. |
Record Nr. |
UNINA9910637780103321 |
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Autore |
Yin Peng-Yeng |
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Titolo |
Applied Metaheuristic Computing |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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ISBN |
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Descrizione fisica |
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1 electronic resource (684 p.) |
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Soggetti |
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Technology: general issues |
History of engineering & technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC. |
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