Neural networks in the physical sciences / Ian Bentley, Marwan Gebran, authors.

Format:

Book

Author/Creator:

Bentley, Ian, Florida Polytechnic University., author.

Gebran, Marwan, Saint Mary's College., author.

Contributor:

American Chemical Society.

Series:

ACS in focus, 2691-8307.

ACS in focus, 2691-8307

Language:

English

Subjects (All):

Neural networks (Computer science)--Scientific applications.

Neural networks (Computer science).

Physical sciences--Data processing.

Physical sciences.

Information storage and retrieval systems--Physical sciences.

Information storage and retrieval systems.

Artificial intelligence--Data processing.

Artificial intelligence.

Genre:

Electronic books.

Physical Description:

1 online resource : illustrations (some color).

Place of Publication:

Washington, DC, USA : American Chemical Society, 2025.

Summary:

"Neural networks and other machine learning techniques have become prevalent in scientific research. The broad use of neural networks marks a paradigm shift resulting from the relatively low price of high-power computing. This allows models theorized decades ago to be implemented across many domains. This primer begins with an introduction to concepts in artificial intelligence and a discussion of terminology. The second and third chapters discuss the architectures used and the mathematics needed to construct these different types of neural networks. This text contains four examples of applications and discussions of the implementation of neural networks relevant to the authors' respective backgrounds; these examples have been provided after the corresponding neural network approach has been introduced. The authors also touch on how these approaches have been implemented in contemporary research. The target audience is scientists, including graduate students, advanced undergraduates, chemists, physicists, and astronomers interested in learning more about this topic. This primer can also help motivate new applications of neural networks in research for problems where the fitting of continuous data or matching of discrete data is of interest."-- Provided by publisher.

Contents:

Introduction to Neural Networks (NN)

Deep Learning Tools

Advanced Neural Network Architectures.

Notes:

Includes bibliographical references and index.

Local Notes:

American Chemical Society, Neural Networks in the Physical Sciences eBooks - 2025 Front Files.

ISBN:

9780841296206

Access Restriction:

Restricted for use by site license.