Introduction to Computation in Physical Sciences

Jay Wang

ISBN 10: 3031176480 / ISBN 13: 9783031176487

Published by Springer, Berlin, Springer International Publishing, Springer, 2024

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a practical and comprehensive introduction to computational problem solving from the viewpoints of practitioners in both academic and industrial worlds. The authors present scientific problem-solving using computation and aim to increase computational thinking, which is the mindset and skillset required to solve scientific problems with computational methodologies via model building, simulation, data analysis, and visualization using the Python programming language. Topics and examples span fundamental areas of physical science as well as contemporary topics including quantum computing, neural networks, machine learning, global warming, and energy balance. The book features unique and innovative techniques and practices including: intentional scaffolding to help beginners learn computational problem solving; multimodal computing environments including cloud-based platforms and just-in-time computing; emphasis and connection between both numerical and symbolic computations; and extensive exercise sets carefully designed for further exploration as project assignments or self-paced study. The book is suitable for introductory level readers in physical sciences, engineering, and related STEM disciplines. Specifically, the book is appropriate for use in either a standalone course on computation and modeling and as a resource for readers interested in learning about proven techniques in interactive computing. 255 pp. Englisch.

Seller Inventory # 9783031176487

Bibliographic Details

• Title: Introduction to Computation in Physical Sciences
• Publisher: Springer, Berlin, Springer International Publishing, Springer
• Publication Date: 2024
• Language: English
• ISBN 10: 3031176480
• ISBN 13: 9783031176487
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 240W x 168H x 14D millimeters 240 millimeters width by 168 millimeters height by 14 millimeters depth
• Weight: 462 grams

About this title

Synopsis

This book provides a practical and comprehensive introduction to computational problem solving from the viewpoints of practitioners in both academic and industrial worlds.  The authors present scientific problem-solving using computation and aim to increase computational thinking, which is the mindset and skillset required to solve scientific problems with computational methodologies via model building, simulation, data analysis, and visualization using the Python programming language.  Topics and examples span fundamental areas of physical science as well as contemporary topics including quantum computing, neural networks, machine learning, global warming, and energy balance. The book features unique and innovative techniques and practices including: intentional scaffolding to help beginners learn computational problem solving; multimodal computing environments including cloud-based platforms and just-in-time computing; emphasis and connection between both numerical and symbolic computations; and extensive exercise sets carefully designed for further exploration as project assignments or self-paced study.  The book is suitable for introductory level readers in physical sciences, engineering, and related STEM disciplines. Specifically, the book is appropriate for use in either a standalone course on computation and modeling and as a resource for readers interested in learning about proven techniques in interactive computing. 

About the Author

Jay J. Wang, Ph.D. is a Professor and Chairperson in the Department of Physics at UMass Dartmouth.  He received his Ph.D. in Theoretical Atomic Physics from the University of Tennessee.  Dr. Wang's interests include computational physics, physics education, and research pertaining to the computation and integration of interactive computation in physics and related STEM curricula.  

Adam L. Wang received his M.S. in Physics from the University of Chicago, where he was awarded an NSF Graduate Research Fellowship.  He has worked on complex systems and nonlinear dynamics, aided by computational tools.  More recently, he uses machine learning, interactive computing, and visualization as a data scientist.