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Yu Maruyama

Publications and source records attributed to Yu Maruyama.

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Perpendicular magnetic anisotropy tuning of macrospin-to-vortex transitions in Co-based artificial spin-vortex ice

We investigate the macrospin-to-vortex (MS-to-V) transition in Co-based artificial spin-vortex ice (ASVI) in the presence of perpendicular magnetic anisotropy (PMA) by spin-wave spectroscopy. Detailed micromagnetic simulations using mumax3 reveal that the PMA modifies the magnetic energy landscape and facilitates vortex formation, suggesting that PMA can enhance the transition probability. To seek experimental validation of this hypothesis, we prepared Ti (3 nm)/Co (10 nm)/Ti (3 nm)/Pt (2 nm) (TCT) and Ti (3 nm)/Co (10 nm)/Pt (2 nm) (TCP) multilayer stacks. Vibrating sample magnetometry measurements confirm that the TCP film exhibits a larger PMA than the TCT film. Using these stacks, we then investigate the MS-to-V transition probability in ASVIs and found that TCP ASVIs exhibit a higher transition probability than TCT ASVIs, in agreement with the simulation prediction. These findings identify PMA as an effective design parameter for controlling vortex formation in ASVIs and provide a promising route toward task-dependent tuning of fading-memory properties for physical reservoir computing based on artificial spin lattices.

cond-mat.mtrl-sci

Global Continuous Optimization with Error Bound and Fast Convergence

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.

math.OC