Introducing students to research codes: A short course on solving partial differential equations in Python

• Published in: Education for chemical engineers Vol. 36; pp. 1 - 11
• Main Authors: Inguva, Pavan, Bhute, Vijesh J., Cheng, Thomas N.H., Walker, Pierre J.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021
• Subjects: Experiential learning; FiPy; Introductory; Mathematics; Partial differential equation; Python; Experiential learning; Mathematics; FiPy; Partial differential equation; Introductory; Python
• ISSN: 1749-7728; 1749-7728
• DOI: 10.1016/j.ece.2021.01.011

•Open-source research-grade codes for solving partial differential equations in Python can be effectively introduced to students as part of a short course.•Effective learning for novice users can be achieved with the provision of adequate support in the form of comprehensive teaching material and guidance during initial set-up and running.•Educators and students can benefit from the increased accessibility of such highperformance tools which can be used for both augmenting the teaching of various subjects and for tackling research and industrial problems.•All code and material from the short course is made available on a public GitHub repository. Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes, FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.