Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Nathan A. Garland, Romit Maulik, Qi Tang, Xian Zhu Tang, Prasanna Balaprakash

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

Original languageEnglish
Article number045003
JournalMachine Learning: Science and Technology
Volume3
Issue number4
DOIs
StatePublished - Dec 1 2022
Externally publishedYes

Funding

This work was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under Contract No. DEAC02–06CH11357, at Argonne National Laboratory, and by the Office of Fusion Energy Sciences and Office of Advanced Scientific Computing Research under the Scientific Discovery through Advanced Computing (SciDAC) project of Tokamak Disruption Simulation at Los Alamos National Laboratory (Contract No. 89233218CNA000001). Partial support was also provided by the Office of Fusion Energy Sciences under the DeepFusion pilot project in scientific machine learning and artificial intelligence for fusion energy sciences. This research was funded in part by the US DOE Laboratory Directed Research and Development (LDRD) program, under Grant No. 20200356ER. This research was funded in part and used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract No. DE-AC02–06CH11357. We acknowledge funding support from ASCR for DOE-FOA-2493 ‘Data-intensive scientific machine learning’.

Keywords

  • active learning
  • adaptive sampling
  • artificial neural network
  • collisional-radiative
  • exploration versus exploitation
  • plasma model
  • surrogate model

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