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This preprint proposes PI-HNO, a physics-informed hybrid neural operator for transient magnetization prediction in power magnetics, achieving low B-H energy consistency errors with only 4,777 trainable parameters per material model.
This paper studies whether the regulatory effort required to stabilize an adaptive agent depends on its history, showing via a hysteresis protocol that the same target can require different control levels depending on the agent's trajectory.
This paper develops a geometric dynamical framework to model how predictive AI assistance alters exploratory cognition by stabilizing trajectories before self-generated exploration, leading to reduced exploratory responsiveness, hysteresis, and delayed recovery upon assistance withdrawal.