Inference and DeNoising of NonGaussian Particle Distribution Functions: A Generative Modeling Approach
Abstract
The particleincell numerical method of plasma physics balances a tradeoff 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 nongaussian and multimodal, 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.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 arXiv:
 arXiv:2110.02153
 Bibcode:
 2021arXiv211002153D
 Keywords:

 Physics  Plasma Physics;
 Computer Science  Machine Learning