Machine Learning for Computational Science and Engineering -- a brief introduction and some critical questions

TL;DR

This paper discusses the integration of machine learning into computational science and engineering, highlighting critical challenges and questions to guide effective adaptation in this interdisciplinary field.

Contribution

It offers a critical perspective on the challenges of applying machine learning to CS&E, emphasizing overlooked issues and providing foundational insights for newcomers.

Findings

Highlights key challenges in ML for CS&E

Provides MATLAB code for basic understanding

Raises critical questions for future research

Abstract

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.