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We formulate and solve the problem of finite horizon minimum control effortsteering of the state probability distribution between prescribed endpointjoints for a finite population of networked noisy nonuniform Kuramotooscillators. We consider both the first and second order stochastic Kuramotomodels. For numerical solution of the associated stochastic optimal control, wepropose combining certain measure-valued proximal recursions and theFeynman-Kac path integral computation. We illustrate the proposed framework vianumerical examples.

Schr\"{o}dinger Meets Kuramoto via Feynman-Kac: Minimum Effort Distribution Steering for Noisy Nonuniform Kuramoto Oscillators