Direct Trajectory-Based Optimization of Particle Confinement for Stellarators in DESC

Unlike their axisymmetric counterparts, stellarators do not inherently guarantee particle confinement due to their non-axisymmetric nature. To make stellarators viable candidates for magnetic confinement fusion reactors, researchers are exploring the quasi-symmetric magnetic fields – a subset of omnigeneous fields– as optimization targets. Traditional approaches rely on surrogate metrics such as effective ripple to assess confinement without explicitly simulating particle trajectories. However, recent advances in GPU computing enable the simultaneous integration of thousands of particle trajectories, making direct trajectory-based optimization feasible. In this work, we implement the guiding center trajectory model in the DESC library. DESC's native GPU support and direct access to magnetic field data, without interpolation, enable efficient optimization of 3D MHD equilibria. To ensure numerical differentiability, we employ gradient clipping to exclude non-confined trajectories from backpropagation. This trajectory-based approach, long limited by computational resources, opens new opportunities to directly optimize for particle confinement and deepen our understanding of confinement physics in stellarators.