相关论文: Tunable stochasticity in an artificial spin networ…

Artificial spin ice systems are metamaterials composed of interacting nanomagnets arranged on a lattice, exhibiting geometrical frustration and emergent phenomena such as monopole excitations. We explore magnetization dynamics and monopole…

We use a biophysical model of a local neuronal circuit to study the implications of synaptic plasticity for the detection of weak sensory stimuli. Networks with fast plastic coupling show behavior consistent with stochastic resonance.…

Conventional magnonic devices use three classes of magnetostatic waves that require detailed manipulation of magnetization structure that makes the design and the device/circuitry scalability difficult tasks. Here, we demonstrate that…

The mechanical behavior of inelastic materials with microstructure is very complex and hard to grasp with heuristic, empirical constitutive models. For this purpose, multiscale, homogenization approaches are often used for performing…

Emerging multiphysics metamaterials offer a distinct possibility for regulating complex physical processes. However, two severe constraints still lower their functionality and tunability. First, multiphysics functionality is fixed once…

Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic…

We present results of numerical experiments for neural networks with stochastic gradient-based optimization with adaptive momentum. This widely applied optimization has proved convergence and practical efficiency, but for long-run training…

Our goal is to develop a design framework for multifunctional mechanical metamaterials that can tune their rigidity while optimizing other desired properties. Towards this goal, we first demonstrate that underconstrained central force…

We present an experimental and numerical study of a terahertz metamaterial with a nonlinear response that is controllable via the relative structural positioning of two stacked split ring resonator arrays. The first array is fabricated on…

The low-energy magnetic configurations of artificial frustrated spin chains are investigated using magnetic force microscopy and micromagnetic simulations. Contrary to most studies on two-dimensional artificial spin systems where…

Two-dimensional arrays of magnetically coupled nanomagnets provide a mesoscopic platform for exploring collective phenomena as well as realizing a broad range of spintronic devices. In particular, the magnetic coupling plays a critical role…

A major limitation of current acoustic metamaterials is that their acoustic properties are either locked into place once fabricated or only modestly tunable, tying them to the particular application for which they are designed. We present…

We show theoretically that a network of superconducting loops and magnetic particles can be used to implement magnonic crystals with tunable magnonic band structures. In our approach, the loops mediate interactions between the particles and…

The programmable artificial lattice, based on the controllability of coupling strengths and the scalability of multiple sites, is desperately desired in engineering metamaterials and exploring fundamental physics. In this work, we…

Strongly-interacting nanomagnetic systems are pivotal across next-generation technologies including reconfigurable magnonics and neuromorphic computation. Controlling magnetisation state and local coupling between neighbouring nanoelements…

Magnonic crystals are prototype magnetic metamaterials designed for the control of spin wave propagation. Conventional magnonic crystals are composed of single domain elements. If magnetization textures, such as domain walls, vortices and…

This work addresses the inverse identification of apparent elastic properties of random heterogeneous materials using machine learning based on artificial neural networks. The proposed neural network-based identification method requires the…

Simulating interactions between deformable bodies is vital in fields like material science, mechanical design, and robotics. While learning-based methods with Graph Neural Networks (GNNs) are effective at solving complex physical systems,…

Here, we explore the role of inherent altermagnetic topology in transverse transport phenomena (such as crystal/anomalous Hall, Nernst, and thermal Hall effects) in several famous altermagnets, including tetragonal…

Motivated by recent progress on quantum engineered Kondo lattices, we numerically investigated the local magnetic properties of a hexagonal Anderson droplet consisting of multiple rings of magnetic atoms periodically arrayed on a triangular…