Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

As we approach the operation of magnetic confinement burning-plasma experiments [3, 4] and as reactor design studies are becoming widespread in the fusion energy community [5-8], the need for reliable, fast and accurate physics models of the confined plasma becomes essential to realize economically-attractive fusion energy. Particularly, accurate physics models for the transport of energy, particles and momentum in the confined plasma are exceptionally important. Fusion power production and reactor efficiency strongly depend on the core pressure gradients that are attained within the operational space of the fusion device. These gradients in kinetic profiles are determined by a balance of the energy, particle and torque input and turbulent and collisional transport processes. The nonlinear gyrokinetic framework for turbulent transport [9, 10] is the gold standard for a rigorous description of micro-turbulence in the plasma core.