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In the last decade many research efforts have been focused on understanding the rheology of disordered materials, and several theoretical predictions have been put forward regarding their yielding behavior. Nevertheless, not many…

Soft Condensed Matter · Physics 2021-06-29 Carlos Villarroel , Gustavo Düring

In this contribution, we present a new Materials Knowledge System framework for microstructure-sensitive predictions of effective stress--strain responses in composite materials. The model is developed for composites with a wide range of…

Materials Science · Physics 2018-12-17 Marat I. Latypov , Laszlo S. Toth , Surya R. Kalidindi

We develop an elasto-plastic description for the transient dynamics prior to steady flow of athermally yielding materials. Our mean-field model not only reproduces the experimentally observed non-linear time dependence of the shear-rate…

Soft Condensed Matter · Physics 2018-02-06 Chen Liu , Kirsten Martens , Jean-Louis Barrat

Amorphous solids yield at a critical value of the strain (in strain controlled experiments); for larger strains the average stress can no longer increase - the system displays an elasto-plastic steady state. A long standing riddle in the…

Soft Condensed Matter · Physics 2016-02-24 Prabhat Jaiswal , Itamar Procaccia , Corrado Rainone , Murari Singh

Predicting the time-dependent yielding of colloidal gels under constant stress enables control of their mechanical stability and transport. Using rotational rheometry, we show that the shear rate of colloidal gels during an early stage of…

Soft Condensed Matter · Physics 2022-09-26 Jae Hyung Cho , Irmgard Bischofberger

This paper studies the application of machine learning in extracting the market implied features from historical risk neutral corporate bond yields. We consider the example of a hypothetical illiquid fixed income market. After choosing a…

Mathematical Finance · Quantitative Finance 2018-06-06 Greg Kirczenow , Ali Fathi , Matt Davison

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the…

Machine Learning · Computer Science 2023-06-21 Renato Luiz de Freitas Cunha , Bruno Silva , Priscilla Barreira Avegliano

The mechanical response of yield-stress materials below the yield point remains a subject of debate. Two of the most widely used constitutive models for these materials offer fundamentally conflicting views: one permits plastic flow at all…

The yield of amorphous solids like metallic glasses under external stress was discussed asserting that it is related to the glass transition by increasing temperature, or that it can be understood using statistical theories of various…

Soft Condensed Matter · Physics 2015-04-22 Valery Ilyin , Itamar Procaccia , Carmel Shor , Murari Singh

The deformation of brittle material is primarily accompanied by micro-cracking and faulting. However, it has often been found that continuum fluid models, usually based on a non-Newtonian viscosity, are applicable. To explain this rheology,…

Materials Science · Physics 2016-12-28 K. Z. Nanjo

A general self-learning metabasin escape (SLME) algorithm~\citep{caoPRE2012} is coupled in this work with continuous shear deformations to probe the yield stress as a function of strain rate and temperature for a binary Lennard-Jones (LJ)…

Computational Physics · Physics 2014-05-13 Penghui Cao , Xi Lin , Harold S. Park

A crucially important material parameter for all amorphous solids is the yield stress, which is the value of the stress for which the material yields to plastic flow when it is strained quasi-statically at zero temperature. It is difficult…

Computational Physics · Physics 2010-11-16 Smarajit Karmakar , Edan Lerner , Itamar Procaccia , Jacques Zylberg

In this paper, we discuss a method to calculate the topology of the rate independent single crystal yield surface for materials with arbitrary slip systems and arbitrary slip strengths. We describe the general problem, as motivated by…

Materials Science · Physics 2025-03-04 Matthew Kasemer , Paul R. Dawson

Based on the atomistic studies presented in Part I we develop analytical yield criteria for single crystals that capture the effect of shear stresses other than the Schmid stress (non-glide stresses) on the shear stress needed for…

Materials Science · Physics 2008-07-18 R. Gröger , V. Racherla , J. L. Bassani , V. Vitek

In this paper, we present a new procedure to automatically generate interpretable hyperelastic material models. This approach is based on symbolic regression which represents an evolutionary algorithm searching for a mathematical model in…

Computational Engineering, Finance, and Science · Computer Science 2022-11-08 Rasul Abdusalamov , Markus Hillgärtner , Mikhail Itskov

We present a comprehensive review of the physical behavior of yield stress materials in soft condensed matter, which encompass a broad range of materials from colloidal assemblies and gels to emulsions and non-Brownian suspensions. All…

Soft Condensed Matter · Physics 2017-08-25 Daniel Bonn , Morton M. Denn , Ludovic Berthier , Thibaut Divoux , Sébastien Manneville

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based…

In order to enhance the modeling of metallic materials behavior in non proportional loadings, a modification of the classical elastic-plastic models including distortion of the yield surface is proposed. The new yield criterion uses the…

Classical Physics · Physics 2016-11-27 Marc Louis Maurice François

We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train…

We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train…