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Size effects have been predicted at the micro- or nano-scale for porous ductile materials from Molecular Dynamics, Discrete Dislocation Dynamics and Continuum Mechanics numerical simulations, as a consequence of Geometrically Necessary…

Materials Science · Physics 2019-10-29 J. M. Scherer , J. Hure

Soft materials (e.g., enveloped viruses, liposomes, membranes and supercooled liquids) simultaneously deform or display collective behaviors, while undergoing atomic scale vibrations and collisions. While the multiple space-time character…

Soft Condensed Matter · Physics 2014-01-03 Peter Ortoleva , Abhishek Singharoy , Stephen Pankavich

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particular obstacle for uncertainty quantifications in CFD problems…

Computational Physics · Physics 2018-04-10 Jian-xun Wang , Christopher J. Roy , Heng Xiao

Flow-induced failure of granular materials is relevant to a broad range of geomechanical applications. Plasticity, which is the inherent failure mechanism of most granular materials, enables large deformations that can invalidate linearised…

Applied Physics · Physics 2019-08-26 Lucy C. Auton , Christopher W. MacMinn

A discrete model describing defects in crystal lattices and having the standard linear anisotropic elasticity as its continuum limit is proposed. The main ingredients entering the model are the elastic stiffness constants of the material…

Materials Science · Physics 2007-05-23 A. Carpio , L. L. Bonilla

We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new probabilistic approach for image modeling capable of density estimation, sampling and tractable inference. DCGMM instances exhibit a CNN-like layered structure, in…

Machine Learning · Computer Science 2022-03-22 Alexander Gepperth

Granular materials -- aggregates of many discrete, disconnected solid particles -- are ubiquitous in natural and industrial settings. Predictive models for their behavior have wide ranging applications, e.g. in defense, mining,…

Soft Condensed Matter · Physics 2023-09-01 Aaron S. Baumgarten , Justin Moreno , Brett Kuwik , Sohanjit Ghosh , Ryan Hurley , K. T. Ramesh

A variational modeling framework for hydraulically induced fracturing of elastic-plastic solids is developed in the present work. The developed variational structure provides a global minimization problem. While fracture propagation is…

Numerical Analysis · Mathematics 2025-04-11 Daniel Kienle , Marc-Andre Keip

We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitations of conventional Jump Gaussian Processes (JGP) in…

Machine Learning · Computer Science 2026-01-16 Yang Xu , Chiwoo Park

Dissipative particle dynamics (DPD) is a well-established mesoscale simulation method. However, there have been long-standing ambiguities regarding the dependence of its (purely repulsive) force field parameter on temperature as well as the…

Chemical Physics · Physics 2019-02-25 Peter Vanya , Jonathan Sharman , James A. Elliott

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic…

Computational Engineering, Finance, and Science · Computer Science 2017-07-17 Charilaos Mylonas , Valentin Bemetz , Eleni Chatzi

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modelling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport…

Fluid Dynamics · Physics 2025-10-22 Marina Echeverria Ferrero , Nicolas Moreno , Marco Ellero

Differentiable particle-based simulation can produce physically plausible motion, but target-driven volumetric shape morphing remains underconstrained: physics-only mass matching captures coarse global structure yet struggles with fine…

Graphics · Computer Science 2026-04-22 Chang-Yong Song , David Hyde

During plastic deformation, metals change shape while continuously becoming stronger. The microscopic origin of these processes lies in the proliferation and movement of line defects, dislocations, and the subsequent self-organisation and…

Multi-fidelity approaches combine different models built on a scarce but accurate data-set (high-fidelity data-set), and a large but approximate one (low-fidelity data-set) in order to improve the prediction accuracy. Gaussian Processes…

Machine Learning · Computer Science 2020-06-30 Ali Hebbal , Loic Brevault , Mathieu Balesdent , El-Ghazali Talbi , Nouredine Melab

The growing number of ceramics exhibiting bulk plasticity at room temperature has renewed interest in revisiting plastic deformation and dislocation-mediated mechanical and functional properties in these materials. In this work, a…

Materials Science · Physics 2026-02-11 Iwo Słodczyk , Alexander Frisch , Xufei Fang , Inna Gitman , Fengxian Liu

Joint utilization of multiple discrete frequency bands can enhance the accuracy of delay estimation. Although some unique challenges of multiband fusion, such as phase distortion, oscillation phenomena, and high-dimensional search, have…

Signal Processing · Electrical Eng. & Systems 2025-07-09 Zhixiang Hu , An Liu , Minjian Zhao

The effects of dislocation climb on plastic deformation during loading and unloading are studied using a two-dimensional discrete dislocation dynamics model. Simulations are performed for polycrystalline thin films passivated on both…

Materials Science · Physics 2014-01-03 Kamyar M. Davoudi , Lucia Nicola , Joost J. Vlassak

The plasticity transition at the yield strength of a crystal typically signifies the tendency of dislocation defects towards relatively unrestricted motion. For an isolated dislocation the motion is in the slip plane with velocity…

Materials Science · Physics 2020-01-15 Péter Dusán Ispánovity , Stefanos Papanikolaou , István Groma

This paper addresses the problem of quantification and propagation of uncertainties associated with dependence modeling when data for characterizing probability models are limited. Practically, the system inputs are often assumed to be…

Computation · Statistics 2020-04-14 Jiaxin Zhang , Michael D. Shields
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