Contemporary machine learning: a guide for practitioners in the physical sciences

TL;DR

This paper provides a comprehensive tutorial on modern machine learning techniques, especially deep learning, tailored for physical sciences researchers, emphasizing practical applications, model generalization, and addressing common challenges.

Contribution

It offers an accessible overview of current machine learning methods, focusing on deep neural networks, for physical sciences practitioners, including guidance on model selection and generalization.

Findings

Deep learning effectively models complex physical science data.

Practitioners can improve model generalization with specific techniques.

Various tasks like regression, image analysis, and time-series fitting are addressed.

Abstract

Machine learning is finding increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. We present a tutorial on current techniques in machine learning -- a jumping-off point for interested researchers to advance their work. We focus on deep neural networks with an emphasis on demystifying deep learning. We begin with background ideas in machine learning and some example applications from current research in plasma physics. We discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated deep learning methods. We also address unsupervised learning and techniques for reducing the dimensionality of input spaces. Along the way, we describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We describe classes of tasks -- predicting scalars, handling images, fitting time-series -- and prepare the reader to choose an appropriate technique. We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help.