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Concurrent multiscale finite element analysis (FE2) is a powerful approach for high-fidelity modeling of materials for which a suitable macroscopic constitutive model is not available. However, the extreme computational effort associated…

Numerical Analysis · Mathematics 2020-07-16 I. B. C. M. Rocha , P. Kerfriden , F. P. van der Meer

Results of recent large-scale molecular dynamics simulations of dislocation-mediated solid plasticity are campared with predictions of the statistical thermodynamic theory of these phenomena. These computational and theoretical analyses are…

Materials Science · Physics 2018-09-12 J. S. Langer

This paper presents PANIC, a 3D discrete mesoscale dislocation dynamics model which includes a fully quantitative treatment of both dislocation climb and dislocation glide, including climb driven by both osmotic and mechanical stresses and…

Materials Science · Physics 2014-06-04 Wai Yuen Fu , Colin J. Humphreys , Michelle A. Moram

Atomic migration from metallic contacts, and subsequent filament formation, is recognised as a prevailing mechanism leading to resistive switching in memristors based on two-dimensional materials (2DMs). This study presents a detailed…

Percolation is a model for random damage to a network. It is one of the simplest models that displays a phase transition: when the network is severely damaged, it falls apart in many small connected components, while if the damage is light,…

Probability · Mathematics 2025-12-18 Remco van der Hofstad

Molecular dynamics simulations are powerful tools to extract the microscopic mechanisms characterizing the properties of soft materials. We recently introduced machine learning surrogates for molecular dynamics simulations of soft materials…

Soft Condensed Matter · Physics 2021-10-29 J. C. S. Kadupitiya , Nasim Anousheh , Vikram Jadhao

Due to the increasing heterogeneity and deployment density of emerging cellular networks, new flexible and scalable approaches for their modeling, simulation, analysis and optimization are needed. Recently, a new approach has been proposed:…

Information Theory · Computer Science 2015-06-15 Wei Lu , Marco Di Renzo

We employ the methods of atomistic simulation to investigate the climb of edge dislocation at nanovoids by analyzing the energetics of the underlying mechanism. A novel simulation strategy has been demonstrated to estimate the release of…

Materials Science · Physics 2013-04-22 A. Dutta , M. Bhattacharya , N. Gayathri , G. C. Das , P. Barat

A model is described, in which electrical breakdown in high-voltage systems is caused by stochastic fluctuations of the mobile dislocation population in the cathode. In this model, the mobile dislocation density normally fluctuates, with a…

Accelerator Physics · Physics 2019-09-05 Eliyahu Zvi Engelberg , Ayelet Badichi Yashar , Yinon Ashkenazy , Michael Assaf , Inna Popov

To study the nanoscopic interaction between edge dislocations and a phase boundary within a two-phase microstructure the effect of the phase contrast on the internal stress field due to the dislocations needs to be taken into account. For…

Materials Science · Physics 2019-09-04 F. Bormann , R. H. J. Peerlings , M. G. D. Geers , B. Svendsen

Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges in the latent graph. Nonetheless, there is increasing…

Methodology · Statistics 2023-12-15 Willem van den Boom , Maria De Iorio , Alexandros Beskos

Real-world graphs, such as social networks, financial transactions, and recommendation systems, often demonstrate dynamic behavior. This phenomenon, known as graph stream, involves the dynamic changes of nodes and the emergence and…

Machine Learning · Computer Science 2023-05-16 Yanping Zheng , Zhewei Wei , Jiajun Liu

The Continuum Dislocation Dynamics (CDD) theory and the Discrete Dislocation Dynamics (DDD) method are compared based on concise mathematical formulations of the coarse graining of discrete data. A numerical tool for converting from a…

Materials Science · Physics 2016-03-03 Stefan Sandfeld , Giacomo Po

This study presents an integrated computational framework that, given synthesis parameters, predicts the resulting microstructural morphology and mechanical response of ceramic aerogel porous materials by combining physics-based simulations…

Computational Engineering, Finance, and Science · Computer Science 2025-06-10 Md Azharul Islam , Dwyer Deighan , Shayan Bhattacharjee , Daniel Tantalo , Pratyush Kumar Singh , David Salac , Danial Faghihi

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains {\em static} in the course of training. Research in recent…

Machine Learning · Computer Science 2020-06-02 Eliav Buchnik , Edith Cohen

We use a constant velocity steered molecular dynamics (SMD) simulation of the stretching of deca-alanine in vacuum to demonstrate a technique that can be used to create surrogate stochastic processes using the time series that come out of…

Statistical Mechanics · Physics 2015-06-25 Christopher P. Calderon

We use three-dimensional discrete dislocation dynamics simulations (DDD) to study the evolution of interfacial dislocation network (IDN) in particle-strengthened alloy systems subjected to constant stress at high temperatures. We have…

Materials Science · Physics 2019-04-19 Tushar Jogi , Saswata Bhattacharya

In this work we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching, surface recombination can dominate the…

Materials Science · Physics 2025-06-12 Angel Yanguas-Gil , Jeffrey W. Elam

Dislocations are the primary carriers of plasticity in metallic material. Understanding the basic mechanisms for dislocation movement is paramount to predicting the material mechanical response. Relying on atomistic simulations, we observe…

Nonstationary Gaussian processes (GPs) are essential for modeling complex, locally heterogeneous spatial data. A common modeling approach is the spatial deformation method that warps the domain to recover isotropy. However, this static…

Machine Learning · Computer Science 2026-05-01 Minghao Gu , Weizhi Lin , Qiang Huang
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