Abstract
The authors ’ interdisciplinary computational methods course uses Python and associated numerical and visualization libraries to enable students to implement simulations for several different course modules, which highlight the breadth and flexibility of Python-powered computational environments. The field of computational science and engineering (CSE) integrates mastery of specific domain sciences with expertise in data structures, algorithms, numerical analysis, programming methodologies, simulation, visualization, data analysis, and performance optimization. The CSE community has embraced Python as a platform for attacking a wide variety of research problems, in part because of Python’s support for easily gluing together tools from different domains to solve complex problems. Many of the same advantages that Python brings to CSE research also make it useful for teaching: Python and its many batteries can help students learn a wide swath of techniques necessary to perform effective CSE research. “Computational Methods for Nonlinear Systems ” is a graduate-level
Keywords
Python (programming language)Computer scienceVisualizationComputational scienceComputational thinkingNonlinear systemProgramming languageSoftware engineeringTheoretical computer scienceArtificial intelligencePhysics
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Publication Info
- Year
- 2007
- Type
- article
- Volume
- 9
- Issue
- 3
- Pages
- 75-79
- Citations
- 19
- Access
- Closed
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Cite This
Christopher R. Myers,
James P. Sethna
(2007).
Python for Education: Computational Methods for Nonlinear Systems.
Computing in Science & Engineering
, 9
(3)
, 75-79.
https://doi.org/10.1109/mcse.2007.56
Identifiers
- DOI
- 10.1109/mcse.2007.56