  Inductively coupled plasma wind tunnels are crucial for replicating
hypersonic flight conditions in ground testing. Achieving the desired
conditions (e.g., stagnation-point heat fluxes and enthalpies during
atmospheric reentry) requires a careful selection of operating inputs, such as
mass flow, gas composition, nozzle geometry, torch power, chamber pressure, and
probing location along the plasma jet. The study presented herein focuses on
the influence of the torch power and chamber pressure on the plasma jet
dynamics within the 350 kW Plasmatron X ICP facility at the University of
Illinois at Urbana-Champaign. A multi-domain analysis of the jet behavior under
selected power-pressure conditions is presented in terms of emitted light
measurements collected using high-speed imaging. We then use Gaussian Process
Regression to develop a data-informed learning framework for predicting
Plasmatron X jet profiles at unseen pressure and power test conditions.
Understanding the physics behind the dynamics of high-enthalpy flows,
particularly plasma jets, is the key to properly design material testing,
perform diagnostics, and develop accurate simulation models
