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The Landau-Ginzburg (LG) model for membranes is numerically studied on triangulated spheres in ${\bf R}^3$. The LG model is in sharp contrast to the model of Helfrich-Polyakov (HP). The reason for this difference is that the curvature…

Statistical Mechanics · Physics 2015-06-18 Hiroshi Koibuchi , Andrey Shobukhov

Clean superconductors with weakly coupled conducting planes have been suggested as promising candidates for observing the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state. We consider here a layered superconductor in a magnetic field of…

Superconductivity · Physics 2007-05-23 U. Klein , D. Rainer , H. Shimahara

The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials. The presented deep energy method (DEM) is self-contained and…

Machine Learning · Computer Science 2022-05-05 Diab W. Abueidda , Seid Koric , Rashid Abu Al-Rub , Corey M. Parrott , Kai A. James , Nahil A. Sobh

We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach…

Numerical Analysis · Mathematics 2024-03-07 Enrico Ballini , Luca Formaggia , Alessio Fumagalli , Anna Scotti , Paolo Zunino

HfO2-based ferroelectric materials are promising for the next generation of memory devices, attracting significant attention. However, their potential applications are significantly limited by fatigue and imprint phenomena, which affect…

Materials Science · Physics 2024-07-30 Muting Xie , Hongyu Yu , Binhua Zhang , Changsong Xu , Hongjun Xiang

The calculation of the polarization in ferroelectric thin films is performed using an analytical solution of the Euler-Lagrange differential equation with boundary conditions with different extrapolation lengths of positive sign on the…

Materials Science · Physics 2007-05-23 M. D. Glinchuk , E. A. Eliseev , V. A. Stephanovich , R. Farhi

In half-filled high Landau levels, two-dimensional electron systems possess collective phases which exhibit a strongly anisotropic resistivity tensor. A weak, but as yet unknown, rotational symmetry-breaking potential native to the host…

Mesoscale and Nanoscale Physics · Physics 2009-11-10 K. B. Cooper , J. P. Eisenstein , L. N. Pfeiffer , K. W. West

Polarons are electronic excitations dressed by a self-consistent lattice distortion, yet their formation has not been directly resolved in real time. We develop a microscopic lineshape framework that connects the growth of a collective…

Other Condensed Matter · Physics 2026-03-02 Priya Nagpal , Arnab Ghosh , Helene Seiler , Samuel Palato , Patanjali Kambhampati

The Landau theory was applied to treat the phase diagrams for a multiferroic with two second order phase transitions taking into account the coupling of the primary order parameters with strain. Two order parameters are coupled…

Strongly Correlated Electrons · Physics 2015-06-18 E. V. Charnaya , A. L. Pirozerskii , K. R. Gabbasova , A. S. Bugaev

Strong magnetic fields quench the kinetic energy of electrons, leading to the formation of flat energy bands, known as Landau levels (LLs). In this situation, even weak interactions can drive the emergence of various ordered phases. The…

In the paper, the description of the dielectric and ferroelectric properties of BaTiO_3 single crystals using Landau thermodynamic potential is addressed. Our results suggest that when using the sixth-power free energy expansion of the…

Statistical Mechanics · Physics 2009-11-13 Y. L. Wang , A. K. Tagantsev , D. Damjanovic , N. Setter , V. K. Yarmarkin , A. I. Sokolov , I. A. Lukyanchuk

The de Haas - van Alphen effect in two-dimensional (2D) metals is investigated at different conditions and with different shapes of Landau levels (LLs). The analytical calculations can be done when many LLs are occupied. We consider the…

Statistical Mechanics · Physics 2007-05-23 P. D. Grigoriev , I. D. Vagner

Evolution of a stripe array of polarization domains triggered by the oxygen vacancy migration in an acceptor doped ferroelectric is investigated in a self-consistent manner. A comprehensive model based on the Landau-Ginzburg-Devonshire…

Materials Science · Physics 2019-07-08 Ivan S. Vorotiahin , Anna N. Morozovska , Yuri A. Genenko

Large-scale deep neural networks consume expensive training costs, but the training results in less-interpretable weight matrices constructing the networks. Here, we propose a mode decomposition learning that can interpret the weight…

Machine Learning · Computer Science 2023-04-13 Chan Li , Haiping Huang

Proximity ferroelectricity is a novel paradigm for inducing ferroelectricity in a non-ferroelectric polar material such as AlN or ZnO that are typically unswitchable with an external field below their dielectric breakdown field. When placed…

Dimerized antiferromagnetic spin-1/2 ladders are known to exhibit a quantum critical phase transition in the ground state, the existence or absence of which is dependent on the dimerization pattern of the ladder. The gapped phases cannot be…

Strongly Correlated Electrons · Physics 2013-05-29 S. J. Gibson , R. Meyer , G. Y. Chitov

Ferroelectric order in polar liquids has been observed in numerical simulations and liquid-crystal experiments. In mean-field frameworks, this behavior is associated to sample-shape dependent, surface contribution to the free energy. This…

Chemical Physics · Physics 2026-02-24 M. G. Izzo

This paper studies DFT models for homogeneous 2D materials in 3D space, under a constant perpendicular magnetic field. We show how to reduce the three--dimensional energy functional to a one--dimensional one, similarly as in our previous…

Mathematical Physics · Physics 2023-07-18 David Gontier , Salma Lahbabi , Abdallah Maichine

Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we…

Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations…

Machine Learning · Computer Science 2024-05-14 Rixin Yu , Erdzan Hodzic , Karl-Johan Nogenmyr
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