Physics-Constrained Neural Flow Maps for Long-Horizon Prediction of Spin Dynamics

Abstract:Conventional simulation of current-driven magnetization relies on fine-step integration of the spin-transfer-torque Landau--Lifshitz--Gilbert equation, creating a computational bottleneck in parameter sweeps and control searches. In this work, we propose a physics-constrained neural flow map that learns finite-time dynamics directly on the unit sphere. The model maps the current magnetization, spin-torque strength, and requested time span to a future state in a single forward pass. Tangent-space projection and spherical retraction preserve unit magnetization during recursive, composition-consistent rollout. We validate the framework on single-spin trajectories under in-domain torques and previously unseen but stronger drive. Beyond the training horizon, it achieves an in-domain root mean square error of $0.00425$ with norm drift at the $10^{-7}$ level. The flow outperforms an adapted Long Short-Term Memory (LSTM) in in-domain accuracy and geometric stability, although the LSTM retains slightly lower out-of-distribution state error. The resulting geometry-preserving propagator reduces reliance on fine-step integration and enables physically admissible long-horizon prediction.

Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Machine Learning (cs.LG)

arXiv:2608.22006 [cond-mat.mes-hall]

(or arXiv:2608.22006v1 [cond-mat.mes-hall] for this version)

https://doi.org/10.48550/arXiv.2608.22006 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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