Integrated Predictive Transport Modeling with Neural Networks and Self-Consistent L-H/H-L Transitions in COTSIM*

COTSIM is a control-oriented predictive simulation code augmented with neural-network surrogate models for plasma sources and transport. A neural network version of the updated Multi-Mode Model (MMM 9.0.10), referred to as MMMnet, predicts turbulent transport with high accuracy and significantly reduced computational cost compared to the conventional MMM. The coupled transport and current-drive equations are evolved self-consistently, incorporating Ohmic heating, ECRH (via TORAYnet), and neutral beam injection (via NUBEAMnet). The magnetic equilibrium is computed in this study using a fixed-boundary solver. Edge pedestal height and width are predicted using the PEDESTAL module, which includes an empirical model for the L-H transition. H-L transitions are triggered when the separatrix power drops below the L-H threshold. The integration of neural-network surrogates and tightly coupled solvers enables fast and accurate predictions of electron and ion temperature and safety factor profiles in DIII-D discharges.