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

12/20/2017
by   Brian K. Spears, et al.
0

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.

READ FULL TEXT

page 17

page 24

research
05/11/2018

Machine Learning for Public Administration Research, with Application to Organizational Reputation

Machine learning methods have gained a great deal of popularity in recen...
research
01/13/2022

The Fairness Field Guide: Perspectives from Social and Formal Sciences

Over the past several years, a slew of different methods to measure the ...
research
03/23/2017

Perspective: Energy Landscapes for Machine Learning

Machine learning techniques are being increasingly used as flexible non-...
research
03/04/2019

Reconstruction of Hydraulic Data by Machine Learning

Numerical simulation models associated with hydraulic engineering take a...
research
05/04/2020

Off-the-shelf deep learning is not enough: parsimony, Bayes and causality

Deep neural networks ("deep learning") have emerged as a technology of c...
research
08/24/2020

Jet Flavour Classification Using DeepJet

Jet flavour classification is of paramount importance for a broad range ...

Please sign up or login with your details

Forgot password? Click here to reset