東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Artificial intelligence for scientif...

Iten, Raban.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for scientific discoveries/ by Raban Iten.
Reminder of title:	extracting physical concepts from experimental data using deep learning /
Author:	Iten, Raban.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xiii, 170 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Artificial intelligence. -
Online resource:	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 /
LDR:02380nmm a2200325 a 4500 001 2318026
003 DE-He213
005 20230411211809.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031270192 $q (electronic bk.)
020 $a 9783031270185 $q (paper)
024 7 $a 10.1007/978-3-031-27019-2 $2 doi
035 $a 978-3-031-27019-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q180.55.D57
072 7 $a PHU $2 bicssc
072 7 $a SCI040000 $2 bisacsh
072 7 $a PHU $2 thema
082 0 4 $a 507.2 $2 23
090 $a Q180.55.D57 $b I88 2023
100 1 $a Iten, Raban. $3 3632668
245 1 0 $a Artificial intelligence for scientific discoveries $h [electronic resource] : $b extracting physical concepts from experimental data using deep learning / $c by Raban Iten.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xiii, 170 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Artificial intelligence. $3 516317
650 0 $a Discoveries in science $x Data processing. $3 3632669
650 1 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Data Analysis and Big Data. $3 3538537
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-27019-2
950 $a Physics and Astronomy (SpringerNature-11651)