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We propose a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision for scientific machine learning under data scarcity. Unlike prior PINNs that rely on fixed or…

Machine Learning · Computer Science 2026-05-08 Reza Pirayeshshirazinezhad

Ferromagnetism in topological insulators (TIs) opens a topologically non-trivial exchange band gap, providing an exciting platform to manipulate the topological order through an external magnetic field. Here, we experimentally show that the…

All-electrical and programmable manipulations of ferromagnetic bits are highly pursued for the aim of high integration and low energy consumption in modern information technology. Methods based on the spin-orbit torque switching in heavy…

In magnetic nanostructures one usually uses a magnetic field to commute between two resistance (R) states. A less common but technologically more interesting alternative to achieve R-switching is to use an electrical current, preferably of…

Materials Science · Physics 2009-11-11 J. Ventura , J. P. Araujo , J. B. Sousa , Y. Liu , Z. Zhang , P. P. Freitas

We present a new physics informed neural network (PINN) algorithm for solving brittle fracture problems. While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the…

Machine Learning · Statistics 2019-07-08 Somdatta Goswami , Cosmin Anitescu , Souvik Chakraborty , Timon Rabczuk

We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle…

Mesoscale and Nanoscale Physics · Physics 2024-03-25 Cuauhtemoc Nuñez Valencia , William Bang Lomholdt , Matthew Helmi Leth Larsen , Thomas W. Hansen , Jakob Schiøtz

Graphene-like two-dimensional (2D) materials, not only are interesting for their exotic electronic structure and fundamental electronic transport or optical properties but also, hold promises for device miniaturization down to atomic…

In this paper, we develop an explicit model to predict the DC electrical behavior in ultra-thin surrounding gate junctionless nanowire FET. The proposed model takes into account 2D electrical and geometrical confinements of carrier charge…

Applied Physics · Physics 2019-06-26 Danial Shafizade , Majid Shalchian , Farzan Jazaeri

First-principles density-functional theory calculations show switching magnetization by 90 degree can be achieved in ultrathin BFO film by applying external electric-field. Up-spin carriers appear to the surface with positive field while…

Materials Science · Physics 2013-02-01 Hong-Jian Feng

Phosphorene is a novel two-dimensional material that can be isolated through mechanical exfoliation from layered black phosphorus, but unlike graphene and silicene, monolayer phosphorene has a large band gap. It was thus unsuspected to…

Materials Science · Physics 2015-02-18 Qihang Liu , Xiuwen Zhang , L. B. Abdalla , A. Fazzio , Alex Zunger

We present formulas for the nuclear and electronic spin relaxation times due to the hyperfine interaction for nanostructed systems and show that the times depend on the square of the local density of electronic states at the nuclear…

Condensed Matter · Physics 2009-11-07 Ionel Tifrea , Michael E. Flatte

In the framework of the London approximation the magnetic flux penetration into a type-II superconductor filament surrounded by a soft-magnet sheath and exposed to a transverse external magnetic field is studied. The lower transverse…

Superconductivity · Physics 2009-11-10 S. V. Yampolskii , Yu. A. Genenko

Conventional nonpolar nematic liquid crystals, widely known for their successful display applications, are not well-suited for fast electro-optical switching because of the slow (milliseconds) field-off relaxation of the director. In this…

Soft Condensed Matter · Physics 2025-07-16 Kamal Thapa , Sathyanarayana Paladugu , Oleg D. Lavrentovich

In this work, we have studied the effects from increasing the strength of the applied electric field on the charge transport of hydrogenated graphitic fibers. Resistivity measurements were carried out for direct currents in the nA - mA…

Materials Science · Physics 2020-05-19 Nadina Gheorghiu , Charles R. Ebbing , Timothy J. Haugan

It is well-known that when the magnetic field is stronger than a critical value, the spin imbalance can break the Cooper pairs of electrons and hence hinder the superconductivity in a spin-singlet channel. In a bilayer system of ultra-cold…

Quantum Gases · Physics 2016-10-13 Jun-Hui Zheng , Daw-Wei Wang , Gediminas Juzeliunas

We theoretically propose a novel spin-dependent electronic transport mechanism in which the spin-unpolarized electron beam is split into different directions depending on spins at an atomic domain boundary in non-magnetic material.…

Mesoscale and Nanoscale Physics · Physics 2015-05-27 Tetsuro Habe , Mikito Koshino

We observe a dependence of the damping of a confined mode of precessing ferromagnetic magnetization on the size of the mode. The micron-scale mode is created within an extended, unpatterned YIG film by means of the intense local dipolar…

Silver nanoparticles embedded in a dielectric matrix are investigated for their potential as broadband-absorbing optical sensor materials. This contribution focuses on the electrical properties of silver nanoparticles at various…

Materials Science · Physics 2011-06-21 Haoyan Wei , Hergen Eilers

Nano-protrusion (NP) on metal surface and its inevitable contamination layer under high electric field is often considered as the primary precursor that leads to vacuum breakdown, which plays an extremely detrimental effect for high energy…

In this paper, we developed a new PINN-based model to predict the potential of point-charged particles surrounded by conductive walls. As a result of the proposed physics-informed neural network model, the mean square error and R2 score are…

Computational Physics · Physics 2023-01-06 Fatemeh Hafezianzade , Morad Biagooi , SeyedEhsan Nedaaee Oskoee
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