Inference and De-Noising of Non-Gaussian Particle Distribution Functions: A Generative Modeling Approach

TL;DR

This paper introduces a generative modeling approach using normalizing flows to accurately infer and de-noise non-Gaussian, multi-modal particle distribution functions in plasma physics simulations, preserving physical properties.

Contribution

It presents a novel application of normalizing flows for modeling dynamic, non-Gaussian particle distributions, improving inference over traditional binning methods.

Findings

Normalizing flows effectively model complex particle distributions.

The approach preserves key physical properties of the data.

It can be extended to capture temporal evolution.

Abstract

The particle-in-cell numerical method of plasma physics balances a trade-off between computational cost and intrinsic noise. Inference on data produced by these simulations generally consists of binning the data to recover the particle distribution function, from which physical processes may be investigated. In addition to containing noise, the distribution function is temporally dynamic and can be non-gaussian and multi-modal, making the task of modeling it difficult. Here we demonstrate the use of normalizing flows to learn a smooth, tractable approximation to the noisy particle distribution function. We demonstrate that the resulting data driven likelihood conserves relevant physics and may be extended to encapsulate the temporal evolution of the distribution function.