Learning Transport Processes with Machine Intelligence
This work addresses the challenge of studying transport phenomena in fields like fusion and cosmic plasmas, where theoretical investigation is impractical, offering a method to derive reliable mathematical expressions for theoretical use.
The authors tackled the problem of modeling transport processes with complex micro-physics, such as heat flux suppression in plasmas, by developing a machine learning model that learns latent representations closer to ground truth than expected from data errors, achieving controllable accuracy through data quality and size.
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.