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Gradient structured (GS) metals processed by severe plastic deformation techniques can be designed to achieve simultaneously high strength and high ductility. Significant kinematic hardening is key to their excellent strain hardening…

Materials Science · Physics 2020-02-11 Jianfeng Zhao , Xiaochong Lu , Jinling Liu , Chen Bao , Guozheng Kang , Michael Zaiser , Xu Zhang

We introduce a symmetric, gradient exclusion process within the class of non-cooperative kinetically constrained lattice gases, modelling a non-linear diffusivity in which the exchange of occupation values between two neighbouring sites…

Probability · Mathematics 2025-04-18 G. S. Nahum

Stochastic gradient methods are the workhorse (algorithms) of large-scale optimization problems in machine learning, signal processing, and other computational sciences and engineering. This paper studies Markov chain gradient descent, a…

Optimization and Control · Mathematics 2018-09-13 Tao Sun , Yuejiao Sun , Wotao Yin

In this paper, we show that simple {Stochastic} subGradient Decent methods with multiple Restarting, named {\bf RSGD}, can achieve a \textit{linear convergence rate} for a class of non-smooth and non-strongly convex optimization problems…

Machine Learning · Computer Science 2016-04-01 Tianbao Yang , Qihang Lin

This article deals with a viscoplastic material model of overstress type. The model is based on a multiplicative decomposition of the deformation gradient into elastic and inelastic part. An additional multiplicative decomposition of…

Numerical Analysis · Mathematics 2015-05-13 A. V. Shutov , R. Kreissig

Energies and equilibrium equations for thin elastic plates are discussed, with emphasis on several issues pertinent to recent approaches in soft condensed matter. Consequences of choice of basis, choice of invariant strain measures, and of…

Soft Condensed Matter · Physics 2019-06-04 J. A. Hanna

In horizontally shaken granular material different types of pattern formation have been reported. We want to deal with the convection instability which has been observed in experiments and which recently has been investigated numerically.…

Materials Science · Physics 2007-05-23 Clara Saluena , Thorsten Poeschel

We present a framework for the multiscale modeling of finite strain magneto-elasticity based on physics-augmented neural networks (NNs). By using a set of problem specific invariants as input, an energy functional as the output and by…

Computational Engineering, Finance, and Science · Computer Science 2023-09-01 Karl A. Kalina , Philipp Gebhart , Jörg Brummund , Lennart Linden , WaiChing Sun , Markus Kästner

We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this template naturally arise from SDP-relaxations of…

Machine Learning · Computer Science 2020-07-09 Maria-Luiza Vladarean , Ahmet Alacaoglu , Ya-Ping Hsieh , Volkan Cevher

The starting assumptions to study the convergence and complexity of gradient-type methods may be the smoothness (also called Lipschitz continuity of gradient) and the strong convexity. In this note, we revisit these two basic properties…

Optimization and Control · Mathematics 2021-11-01 Lu Zhang , Jiani Wang , Hui Zhang

Nature provides examples of self-assemble lightweight disordered network structures with remarkable mechanical properties which are desirable for many applications purposes but challenging to reproduce artificially. Previous experimental…

Soft Condensed Matter · Physics 2019-03-27 Daniel Rayneau-Kirkhope , Silvia Bonfanti , Stefano Zapperi

We establish several gradient estimates for second-order divergence type parabolic and elliptic systems. The coefficients and data are assumed to be H\"older or Dini continuous in the time variable and all but one spatial variables. This…

Analysis of PDEs · Mathematics 2012-01-26 Hongjie Dong

Accurate constitutive models of soft materials are crucial for understanding their mechanical behavior and ensuring reliable predictions in the design process. To this end, scientific machine learning research has produced flexible and…

Computational Engineering, Finance, and Science · Computer Science 2025-03-04 Adrian Buganza Tepole , Asghar Jadoon , Manuel Rausch , Jan N. Fuhg

We propose an optimization proxy in terms of iterative implicit gradient methods for solving constrained optimization problems with nonconvex loss functions. This framework can be applied to a broad range of machine learning settings,…

Optimization and Control · Mathematics 2025-10-14 Harshal D. Kaushik , Ming Jin

The article addresses the mathematical modeling of the folding of a thin elastic sheet along a prescribed curved arc. A rigorous model reduction from a general hyperelastic material description is carried out under appropriate scaling…

Numerical Analysis · Mathematics 2022-02-09 Sören Bartels , Andrea Bonito , Peter Hornung

Real-world solids, such as rocks, soft tissues, and engineering materials, are often under some form of stress. Most real materials are also, to some degree, anisotropic due to their microstructure, a characteristic often called the…

Classical Physics · Physics 2022-08-09 Soumya Mukherjee , Michel Destrade , Artur L. Gower

The study is devoted to geometrically non-linear modelling of viscoplastic structures with residual stresses. We advocate and develop a special approach to residual stresses based on the transition between reference configurations. The…

Materials Science · Physics 2021-04-06 I. I. Tagiltsev , A. V. Shutov

It is interesting to study the stress concentration between two adjacent stiff inclusions in composite materials, which can be modeled by the Lam\'e system with partially infinite coefficients. To overcome the difficulty from the lack of…

Analysis of PDEs · Mathematics 2018-02-06 Yuanyuan Hou , Hongjie Ju , Haigang Li

This article is devoted to the study of the asymptotic behavior of the zero-energy deformations set of a periodic nonlinear composite material. We approach the problem using two-scale Young measures. We apply our analysis to show that…

Analysis of PDEs · Mathematics 2007-05-23 Marco Barchiesi

Successful modern generalized gradient approximations (GGA) are biased toward atomic energies. Restoration of the first-principles gradient expansion for the exchange energy over a wide range of density gradients eliminates this bias. We…