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Seemingly accurate physics-informed surrogates can fail spectacularly when integrated into power system simulations, especially under stress, highlighting the need for rigorous in-simulator validation.
Unlock up to 59x cost reductions in optimization by pretraining ML surrogates with cheap, imperfect labels and then refining them with self-supervision.
