Introduction to Computation in Physical Sciences: Interactive Computing and Visualization with Python(tm) (物理科學計算導論:使用Python進行互動計算與視覺化)
Wang, Jay, Wang, Adam
相關主題
商品描述
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.
商品描述(中文翻譯)
本書從學術界和工業界的實務者角度,提供了一個實用且全面的計算問題解決入門介紹。作者展示了使用計算進行科學問題解決的方法,並旨在提升計算思維,這是利用模型建構、模擬、數據分析和可視化等計算方法解決科學問題所需的心態和技能,並使用 Python 程式語言進行實作。書中涵蓋的主題和範例橫跨物理科學的基本領域,以及當代主題,包括量子計算、神經網絡、機器學習、全球暖化和能量平衡。本書特色在於獨特且創新的技術和實踐,包括:有意識的支架設計以幫助初學者學習計算問題解決;多模態計算環境,包括雲端平台和即時計算;強調數值計算和符號計算之間的聯繫;以及精心設計的廣泛練習題集,適合進一步探索,作為專案作業或自學使用。本書適合物理科學、工程及相關 STEM 學科的入門讀者。具體而言,本書適合用於獨立的計算與建模課程,或作為有興趣學習互動計算中已證實技術的讀者的資源。
作者簡介
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.
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.
作者簡介(中文翻譯)
Jay J. Wang, Ph.D. 是麻省達特茅斯大學物理系的教授及系主任。他在田納西大學獲得理論原子物理的博士學位。Wang 博士的研究興趣包括計算物理、物理教育,以及與物理和相關 STEM 課程中互動計算的計算與整合相關的研究。
Adam L. Wang 在芝加哥大學獲得物理碩士學位,並獲得 NSF 研究生研究獎學金。他曾在複雜系統和非線性動力學方面工作,並利用計算工具進行研究。最近,他作為數據科學家使用機器學習、互動計算和可視化技術。