Machine Learning Spike Trains of Uneven Duration and Delay: STUD Pulses for Laser-Plasma Instability Control and Suppression

We will show how the laser-plasma instability control and suppression problem can be translated into an inverse problem of discovering the strong space-time modulation profile of an incident laser to mimic or reproduce a certain Stimulated Raman Scattering (SRS) reflectivity or hot electron generation profile, that is deemed desirable or acceptble for a given IFE scheme. How to execute this inverse learning program, how to optimize the seach, and guide the vast terrain exploration of hights, widths, and spacings between laser spikes in a spike trrain, and mapping this to desirable plasma responses will be our goal. Many techniques can be used to simplify or speed up these tasks such as Koopman operator linearlization and spectral analysis, Dynamic Mode Decomposition, suppression of strong turbulence and the relaxation to weak (or wave) turbulence regimes, suppression of intermittency, and more generally kinetic plasma phase space control. This program can be executed on very sophisticated kinetic models of SRS or with mere PIC codes or yet simpler PDE and ODE models in one space and/or one time dimension. The question is how well will transfer learning work among these models and real world data obtained in different laser and plasma conditions. We will turn to ML to explore for answers.