Related papers: Magnetic Hysteresis Modeling with Neural Operators
Modal synthesis methods are a long-standing approach for modelling distributed musical systems. In some cases extensions are possible in order to handle geometric nonlinearities. One such case is the high-amplitude vibration of a string,…
Autoencoders have been widely used for dimensional reduction and feature extraction. Various types of autoencoders have been proposed by introducing regularization terms. Most of these regularizations improve representation learning by…
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining…
This work introduces a neural operator based surrogate modeling framework for neutron transport computation. Two architectures, the Deep Operator Network (DeepONet) and the Fourier Neural Operator (FNO), were trained for fixed source…
Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while…
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural network for a global…
We study hysteresis in a micron-sized bead: a non-magnetic matrix embedded with super- paramagnetic nanoparticles. These hold tremendous promise in therapeutic applications as heat generating machines. The theoretical formulation uses a…
Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…
The traditional magnetic storage mechanisms (both analog and digital) apply an external field signal H(t) to a hysteretic magnetic material, and read the remanent magnetization M(t), which is (roughly) proportional to H(t). We propose a new…
Magnetic moments near zigzag edges in graphene allow complex nanostructures with customised spin properties to be realised. However, computational costs restrict theoretical investigations to small or perfectly periodic structures. Here we…
Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are coupled together for predicting the system's…
Magnetoresistive random-access memory (MRAM) is poised to become a next-generation information storage device. Yet, many materials challenges remain unsolved before it can become a widely used memory storage solution. Among them, an urgent…
Judging by its omnipresence in the literature, the hysteresis observed in the transfer characteristics of emerging transistors based on 2D-materials is widely accepted as an important metric related to the device quality. The hysteresis is…
In recent years, commercial HTS superconductors have gained an increasing interest for their use in applications involving large-scale superconductor systems. These systems are typically made from hundreds to thousands of turns of…
The method of choice to study one-dimensional strongly interacting many body quantum systems is based on matrix product states and operators. Such method allows to explore the most relevant, and numerically manageable, portion of an…
Neural Operators that directly learn mappings between function spaces, such as Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs), have received considerable attention. Despite the universal approximation guarantees for DONs…
Data generated by edge devices has the potential to train intelligent autonomous systems across various domains. Despite the emergence of diverse machine learning approaches addressing privacy concerns and utilizing distributed data,…
Magnetars are conjectured to be highly magnetized neutron stars (NSs). Strong internal magnetic field and elasticity in the crust may deform the stars and lead to free precession. We study the precession dynamics of triaxially-deformed NSs…
We consider a recently introduced 2-D square-to-rectangle martensite model that explains several unusual features of martensites to study ferromagnetic martensites. The strain order parameter is coupled to the magnetic order parameter…
Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for large-scale or online applications. We propose a…