Physics > Plasma Physics
[Submitted on 27 Sep 2021 (v1), last revised 14 Jun 2022 (this version, v3)]
Title:Learning Transport Processes with Machine Intelligence
View PDFAbstract: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.
Submission history
From: Gianluca Gregori [view email][v1] Mon, 27 Sep 2021 14:49:22 UTC (980 KB)
[v2] Sat, 20 Nov 2021 10:02:38 UTC (1,022 KB)
[v3] Tue, 14 Jun 2022 23:01:05 UTC (1,255 KB)
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