Machine Learning for Computational Science and Engineering -- a brief introduction and some critical questions
It addresses the problem of thoughtless ML adaptation in CS&E for researchers and practitioners, but it is incremental as it focuses on raising awareness rather than introducing new methods.
This paper tackles the issue of adapting machine learning (ML) for computational science and engineering (CS&E) by highlighting critical questions and challenges often overlooked, aiming to provide insights for general audiences and new researchers.
Artificial Intelligence (AI) is now entering every sub-field of science, technology, engineering, arts, and management. Thanks to the hype and availability of research funds, it is being adapted in many fields without much thought. Computational Science and Engineering (CS&E) is one such sub-field. By highlighting some critical questions around the issues and challenges in adapting Machine Learning (ML) for CS&E, most of which are often overlooked in journal papers, this contribution hopes to offer some insights into the adaptation of ML for applications in CS\&E and related fields. This is a general-purpose article written for a general audience and researchers new to the fields of ML and/or CS\&E. This work focuses only on the forward problems in computational science and engineering. Some basic equations and MATLAB code are also provided to help the reader understand the basics.