Related papers: Description of collective magnetization processes …
We use a machine learning approach to identify the importance of microstructure characteristics in causing magnetization reversal in ideally structured large-grained Nd$_2$Fe$_{14}$B permanent magnets. The embedded Stoner-Wohlfarth method…
The Stoner-Wohlfarth is the most used model of magnetic hysteresis, but its computation is time-consuming. We use machine learning to approximate piecewise this model by easy-to-compute analytic functions. Our parametrization is suitable…
We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from…
The Stoner-Wohlfarth (SW) model is a classical model for magnetic hysteresis of single-domain particles. For two-dimensional magnets at finite temperature, the SW model must be extended to include intrinsic strong spin fluctuations. We…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional space. With convolutional autoencoders a compression ratio…
Recent advances in high-density magnetic storage and spin electronics are based on the use of magnetic materials along with conventional microelectronic materials (metals, insulators and semiconductors). The unit information (bit) is stored…
In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlinearity. Recent advances in computation have rendered previously computationally infeasible analyses readily executable on common computer…
We establish a time-stepping learning algorithm and apply it to predict the solution of the partial differential equation of motion in micromagnetism as a dynamical system depending on the external field as parameter. The data-driven…
The Stoner-Wohlfarth model provides an efficient analytical model to describe the behavior of magnetic layers within xMR sensors. Combined with a proper description of magneto-resistivity an efficient device model can be derived, which is…
A method for estimation of reversible and irreversible susceptibilities of initial magnetization curves has been developed. It deals only with the energy necessary for magnetizing and demagnetizing the sample, not with the nature of the…
We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural…
In this work, we propose a machine learning-based approach to address a specific aspect of the Quantum Marginal Problem: reconstructing a global density matrix compatible with a given set of quantum marginals. Our method integrates a…
We calculate numerically the magnetization direction as function of magnetic field in the Stoner-Wohlfart theory and are able to reproduce the shape of the low-field magnetoresistance hysteresis observed in manganite grain boundary…
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference…
Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simple distribution assumptions or…
We propose a modular framework for multi-relational learning via tensor decomposition. In our learning setting, the training data contains multiple types of relationships among a set of objects, which we represent by a sparse three-mode…
This paper proposes a novel method for learning highly nonlinear, multivariate functions from examples. Our method takes advantage of the property that continuous functions can be approximated by polynomials, which in turn are representable…
Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal…
In this article a detailed characterization of a magnetization motion in a single sub-micrometer and multi-terminal ferromagnetic structure in lateral geometry is performed in a GHz regime using direct DC characterization technique. We have…