Learning Transport Processes with Machine Intelligence

TL;DR

This paper introduces a machine learning approach to model complex transport processes in physics, demonstrating improved accuracy and generalization, with applications to heat flux suppression in plasma physics.

Contribution

It presents a simple, effective machine learning model that learns accurate latent representations of transport phenomena, surpassing nominal data errors and enabling reliable mathematical descriptions.

Findings

The model achieves closer-to-ground-truth representations than expected from data error levels.

The accuracy of the learned model is controllable via data quality and dataset size.

The approach generalizes beyond specific assumptions, applicable to various transport phenomena.

Abstract

We present a machine learning based approach to address the study of transport processes, ubiquitous in continuous mechanics, with particular attention to those phenomena ruled by complex micro-physics, impractical to theoretical investigation, yet exhibiting emergent behavior describable by a closed mathematical expression. Our machine learning model, built using simple components and following a few well established practices, is capable of learning latent representations of the transport process substantially closer to the ground truth than expected from the nominal error characterising the data, leading to sound generalisation properties. This is demonstrated through an idealized study of the long standing problem of heat flux suppression relevant to fusion and cosmic plasmas. Our analysis shows that the result applies beyond those case specific assumptions and that, in particular, the accuracy of the learned representation is controllable through knowledge of the data quality (error properties) and a suitable choice of the dataset size. While the learned representation can be used as a plug-in for numerical modeling purposes, it can also be leveraged with the above error analysis to obtain reliable mathematical expressions describing the transport mechanism and of great theoretical value.