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Item #:
87866601
Numerical Methods in Physics with Python
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Item #:
87866601
PKR 22483
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This is an ideal standalone textbook for courses on computational physics.
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What Stands Out
Comprehensive Coverage
This edition offers an in-depth exploration of numerical methods specifically tailored for physics applications, ensuring that users grasp complex concepts effectively.
Python Integration
Seamless integration of Python throughout the text allows readers to apply numerical methods practically, enhancing both learning and software skills relevant in modern scientific computing.
Updated Examples
The second edition features revised examples and exercises that reflect current trends in physics, providing students with relevant, hands-on experience to solve real-world problems.
Product Details
| Publisher | Cambridge University Press |
| Publication date | July 20, 2023 |
| Edition | 2nd |
| Language | English |
| Print length | 706 pages |
| ISBN-10 | 1009303864 |
| ISBN-13 | 978-1009303866 |
| Item Weight | 2.9 pounds (1.32 kg) |
| Dimensions | 7 x 1.59 x 10 inches (17.8 x 4 x 25.4 cm) |
Who Should Buy?
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Physics Students
Ideal for undergraduate physics students seeking to understand numerical methods and computational tools in their studies.
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Python Programmers
Beneficial for programmers wanting to apply Python in solving complex physics problems through numerical methods.
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Researchers
Useful for researchers in physics who require numerical techniques for simulations and modeling in their projects.
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Complete Beginners
Not suitable for those without prior knowledge of Python or numerical methods, as it requires foundational understanding.
Product Description
Numerical Methods in Physics with Python
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PKR 22483
Order now and get it around Friday, October 09
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Features & Benefits
- Combines idiomatic Python programming with foundational numerical methods.
- Covers a comprehensive range of numerical methods used in physics.
- Includes 140 new problems and several new codes for enhanced learning.
- Features new sections on advanced topics like neural networks and Bayesian regression.
- Each chapter concludes with an in-depth project addressing complex physics problems.
- Perfect for students new to computational physics, facilitating quick mastery of Python.
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