Computational physics: problem solving with python - 3. edición 🔍
Rubin H. Landau, Manuel J P?ez, Cristian C. Bordeianu
Wiley-VCH; Wiley-Interscience, Physics textbook, 3rd completely revised ed, Weinheim, 2015
English [en] · PDF · 19.1MB · 2015 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
description
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.
The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.
The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
Alternative filename
lgli/978-3-527-41315-7 Computational Physics.pdf
Alternative filename
lgrsnf/978-3-527-41315-7 Computational Physics.pdf
Alternative author
Landau, Rubin H., Páez, Manuel J, Bordeianu, Cristian C.
Alternative author
Manuel J. Pez; Rubin H. Landau; Cristian C. Bordeianu
Alternative publisher
Wiley-VCH-Verl
Alternative publisher
Wiley-VCH GmbH
Alternative edition
3rd completely revised edition, Weinheim, Germany, 2015
Alternative edition
EBL-Schweitzer, 3rd ed, Hoboken, 2015
Alternative edition
Germany, Germany
metadata comments
0
metadata comments
lg1516937
metadata comments
{"edition":"3","isbns":["3527413154","9783527413157"],"last_page":644,"publisher":"Wiley"}
date open sourced
2016-06-17
🚀 Fast downloads
Become a member to support the long-term preservation of books, papers, and more. To show our gratitude for your support, you get fast downloads. ❤️
- Fast Partner Server #1 (recommended)
- Fast Partner Server #2 (recommended)
- Fast Partner Server #3 (recommended)
- Fast Partner Server #4 (recommended)
- Fast Partner Server #5 (recommended)
- Fast Partner Server #6 (recommended)
- Fast Partner Server #7
- Fast Partner Server #8
- Fast Partner Server #9
- Fast Partner Server #10
- Fast Partner Server #11
- Fast Partner Server #12
🐢 Slow downloads
From trusted partners. More information in the FAQ. (might require browser verification — unlimited downloads!)
- Slow Partner Server #1 (slightly faster but with waitlist)
- Slow Partner Server #2 (slightly faster but with waitlist)
- Slow Partner Server #3 (slightly faster but with waitlist)
- Slow Partner Server #4 (slightly faster but with waitlist)
- Slow Partner Server #5 (no waitlist, but can be very slow)
- Slow Partner Server #6 (no waitlist, but can be very slow)
- Slow Partner Server #7 (no waitlist, but can be very slow)
- Slow Partner Server #8 (no waitlist, but can be very slow)
- Slow Partner Server #9 (no waitlist, but can be very slow)
- After downloading: Open in our viewer
External downloads
-
For large files, we recommend using a download manager to prevent interruptions.
Recommended download managers: JDownloader -
You will need an ebook or PDF reader to open the file, depending on the file format.
Recommended ebook readers: Anna’s Archive online viewer, ReadEra, and Calibre -
Use online tools to convert between formats.
Recommended conversion tools: CloudConvert and PrintFriendly -
You can send both PDF and EPUB files to your Kindle or Kobo eReader.
Recommended tools: Amazon‘s “Send to Kindle” and djazz‘s “Send to Kobo/Kindle” -
Support authors and libraries
✍️ If you like this and can afford it, consider buying the original, or supporting the authors directly.
📚 If this is available at your local library, consider borrowing it for free there.
Total downloads:
A “file MD5” is a hash that gets computed from the file contents, and is reasonably unique based on that content. All shadow libraries that we have indexed on here primarily use MD5s to identify files.
A file might appear in multiple shadow libraries. For information about the various datasets that we have compiled, see the Datasets page.
For information about this particular file, check out its JSON file. Live/debug JSON version. Live/debug page.