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

• URL: http://arxiv.org/abs/2112.12054v1
• Date: Wed, 22 Dec 2021 17:25:32 GMT
• Title: Machine Learning for Computational Science and Engineering -- a brief introduction and some critical questions
• Authors: Chennakesava Kadapa
• Abstract summary: This is a general-purpose article written for a general audience and researchers new to the fields of Machine Learning and/or Computational Science and Engineering. Some basic equations and code are also provided to help the reader understand the basics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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