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This paper introduces a deterministic hyperparameter selection method for reservoir computing using free-probability kernels, which eliminates the need for resource-intensive rollouts and achieves performance similar to exhaustive search with significantly lower cost.
This paper presents the first application of calibration models for offline hyperparameter selection in a real-world industrial setting, using a municipal water treatment plant, and shows they can generate realistic rollouts and recover hyperparameter sensitivity trends.