Introduction to Computation in Physical Sciences

Jay Wang

ISBN 10: 3031176480 / ISBN 13: 9783031176487

Verlag: Springer, Berlin, Springer International Publishing, Springer, 2024

Beschreibung

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.

Bestandsnummer des Verkäufers 9783031176487

Bibliografische Details

• Titel: Introduction to Computation in Physical Sciences
• Verlag: Springer, Berlin, Springer International Publishing, Springer
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 3031176480
• ISBN-13: 9783031176487
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 240B x 168 H x 14 T Millimeter Breite: 240 mm, Höhe: 168 mm, Tiefe: 14 mm
• Gewicht: 462 Gramm

Über diesen Titel

Inhaltsangabe

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. 

Über die Autorin bzw. den Autor

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.