Work

An Interdisciplinary Elective Course to Build Computational Skills for Mathematical Modeling in Science and Engineering

Year: 2020

Type: conference-paper

Abstract: A cross-listed upper division and graduate elective course for students in science, technology, engineering, and mathematics (STEM) fields has been developed to build computational skills in mathematical modeling.The course aims to fill a gap in the practical training of students starting computational research projects across various STEM disciplines who have inconsistent previous experiences in computer programming and numerical methods.This is achieved by covering modern software tools for mathematical modeling in science and engineering and for reproducible research computing via an active, hands-on approach supplemented by reading materials.Rather than covering just the basics of programming or detailed algorithms for numerical methods, the course is geared towards implementing tools for solving realistic continuum scale science and engineering problems, managing open source code projects, and disseminating computational research results through scientific documentation and publications.The course is taught by a chemical engineering faculty member with research expertise in applied mathematics and computational science and engineering.MATLAB and Python are taught side-by-side throughout the course.The paper describes the course with the goal of enabling other educators to adapt and reuse the course content.

Source: ASEE Annual Conference & Exposition

DOI: 10.18260/1-2--32072

Author: Ashlee Ford Versypt

Institutions: Oklahoma State University, University of Illinois Urbana-Champaign

Language: English

Topic: Computational Physics and Python Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: National Science Foundation