  The path of tokamak fusion and ITER is maintaining high-performance plasma to
produce sufficient fusion power. This effort is hindered by the transient
energy burst arising from the instabilities at the boundary of high-confinement
plasmas. The application of 3D magnetic perturbations is the method in ITER and
possibly in future fusion power plants to suppress this instability and avoid
energy busts damaging the device. Unfortunately, the conventional use of the 3D
field in tokamaks typically leads to degraded fusion performance and an
increased risk of other plasma instabilities, two severe issues for reactor
implementation. In this work, we present an innovative 3D field optimization,
exploiting machine learning, real-time adaptability, and multi-device
capabilities to overcome these limitations. This integrated scheme is
successfully deployed on DIII-D and KSTAR tokamaks, consistently achieving
reactor-relevant core confinement and the highest fusion performance without
triggering damaging instabilities or bursts while demonstrating ITER-relevant
automated 3D optimization for the first time. This is enabled both by advances
in the physics understanding of self-organized transport in the plasma edge and
by advances in machine-learning technology, which is used to optimize the 3D
field spectrum for automated management of a volatile and complex system. These
findings establish real-time adaptive 3D field optimization as a crucial tool
for ITER and future reactors to maximize fusion performance while
simultaneously minimizing damage to machine components.
