This repository contains Jupyter Notebooks with Python code for visualizing examples from the "Advanced Engineering Mathematics" textbook by Dennis G. Zill (Jones & Bartlett Learning). These visualizations are intended to supplement the textbook and help students gain a deeper understanding of key concepts through interactive examples.
Textbook:
- Title: Advanced Engineering Mathematics link
- Author: Dennis G. Zill
- Publisher: Jones & Bartlett Learning
Disclaimer:
This repository is purely for educational purposes and is not intended to replace the textbook. The visualizations are based on examples from the textbook, and it is highly recommended that students purchase the textbook for comprehensive explanations, practice problems, and a complete learning experience.
Contents:
This repository currently includes visualizations for the following chapters:
-
Chapter 9: Vector Calculus
- 9.1: Vector Functions
- 9.2: Motion on a Curve
- 9.3: Curvature and Components of Acceleration
- 9.4: Partial Derivative
- 9.5: Directional Derivative
- 9.6: Tangent Planes and Normal Lines
- 9.7: Curl and Divergence
- 9.8: Line Integrals
- 9.9: Independence of the Path
- 9.12: Green's Theorem
-
Chapter 12: Orthogonal Functions and Fourier Series
- 12.1: Orthogonal Functions
- 12.2: Fourier Series
- 12.3: Fourier Cosine and Sine Series
Usage:
- Access: You can access the notebooks directly on GitHub or open them in Google Colab for interactive use.
- Google Colab: Click on the "Open in Colab" badge at the top of each notebook to open it in Google Colab.
- Interact: Run the code cells in the notebooks to generate the visualizations and experiment with different parameters.
Contributing:
If you find any errors, have suggestions for improvements, or would like to contribute additional visualizations, please feel free to submit an issue or a pull request.
License:
This repository is licensed under the BSD 3-Clause License + Do Not Harm.
Acknowledgments:
- Dennis G. Zill for his excellent textbook, "Advanced Engineering Mathematics."
- The developers of Jupyter Notebook, Google Colab and scientific python for providing these valuable tools.