Inference and De-Noising of Non-Gaussian Particle Distribution Functions: A Generative Modeling Approach
This addresses noise and modeling challenges in plasma physics simulations, but it is incremental as it applies an existing generative method to a specific domain problem.
The paper tackled the problem of noisy, non-Gaussian particle distribution functions in plasma physics simulations by using normalizing flows to learn a smooth approximation, resulting in a data-driven likelihood that conserves physics and can model temporal evolution.
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.