arXiv subjects

T. J. Hayward

Publications and source records attributed to T. J. Hayward.

4 recordsLinked to original sources

arXiv2026

Reservoir Computing with Heterogeneous Magnetic Metamaterials

Physical reservoir computing utilizes the intrinsic nonlinear and history-dependent dynamics of physical systems to perform machine-learning tasks with minimal training overhead. Here, we introduce a nanomagnetic reservoir computer based on a heterogeneous array of interconnected magnetic nanorings, combined with multi-channel planar Hall effect readout. The device comprises subarrays of rings with systematically varied track widths ranging from 500 nm to 300 nm, enabling access to the heterogeneous dynamics of geometrically diverse magnetic systems within a single reservoir. By applying time-varying input signals as modulations of a driving rotating magnetic field, we evaluate the nanoring reservoir's performance on nonlinear signal transformation and Mackey-Glass time-series prediction tasks. We find that combining outputs from multiple width-dependent channels significantly reduces the normalized root-mean-square error compared to single-channel readout, with the optimal channel combinations depending on task requirements. These results demonstrate that geometric heterogeneity provides an additional, experimentally accessible degree of freedom and complementary computational features. Principal component analysis further reveals that a reduced subset of correlated features captures most of the computationally relevant information while suppressing noise contributions. These results demonstrate that controlled geometric heterogeneity enhances reservoir expressivity and suggest a route toward scalable magnetic computing architectures in which multi-output magnetic metamaterials serve as configurable dynamical building blocks for device networks.

cs.ET↗

arXiv2026

Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning

We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model-based reinforcement learning (RL) framework. Through a sequence of simulations, we show that neural SDEs more effectively capture the inherent stochasticity of transition dynamics, enabling high-performing policies with improved sample efficiency in challenging scenarios. We leverage neural ODEs and SDEs for efficient policy adaptation to changes in environment dynamics via inverse models, requiring only limited interactions with the new environment. To address partial observability, we introduce a latent SDE model that combines an ODE with a GAN-trained stochastic component in latent space. Policies derived from this model provide a strong baseline, outperforming or matching general model-based and model-free approaches across stochastic continuous-control benchmarks. This work demonstrates the applicability of action-conditional latent SDEs for RL planning in environments with stochastic transitions. Our code is available at: https://github.com/ChaoHan-UoS/NeuralRL

cs.LG↗

arXiv2023

Magnetic domain walls : Types, processes and applications

Domain walls (DWs) in magnetic nanowires are promising candidates for a variety of applications including Boolean/unconventional logic, memories, in-memory computing as well as magnetic sensors and biomagnetic implementations. They show rich physical behaviour and are controllable using a number of methods including magnetic fields, charge and spin currents and spin-orbit torques. In this review, we detail types of domain walls in ferromagnetic nanowires and describe processes of manipulating their state. We look at the state of the art of DW applications and give our take on the their current status, technological feasibility and challenges.

cond-mat.mes-hall↗

arXiv2010

Design and Characterization of a Field-Switchable Nanomagnetic Atom Mirror

We present a design for a switchable nanomagnetic atom mirror formed by an array of 180° domain walls confined within Ni80Fe20 planar nanowires. A simple analytical model is developed which allows the magnetic field produced by the domain wall array to be calculated. This model is then used to optimize the geometry of the nanowires so as to maximize the reflectivity of the atom mirror. We then describe the fabrication of a nanowire array and characterize its magnetic behavior using magneto-optic Kerr effect magnetometry, scanning Hall probe microscopy and micromagnetic simulations, demonstrating how the mobility of the domain walls allow the atom mirror to be switched "on" and "off" in a manner which would be impossible for conventional designs. Finally, we model the reflection of 87Rb atoms from the atom mirror's surface, showing that our design is well suited for investigating interactions between domain walls and cold atoms.

cond-mat.mes-hall↗