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This article presents a survey of NSTX cases to study the microtearing mode(MTM) stabilities using the newly developed global reduced model for Slab-LikeMicrotearing modes (SLiM). A trained neutral network version of SLiM enablesrapid assessment (0.05s/mode) of MTM with $98\%$ accuracy providing anopportunity for systemic equilibrium reconstructions based on the matching ofexperimentally observed frequency bands and SLiM prediction across a wide rangeof parameters. Such a method finds some success in the NSTX discharges, thefrequency observed in the experiment matches with what SLiM predicted. Based onthe experience with SLiM analysis, a workflow to estimate the potential MTMfrequency for a quick assessment based on experimental observation has beenestablished.

Microtearding mode study in NSTX using machine learning enhanced reduced model