Výsledky vyhledávání - "Gupta, Ashwini"

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Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network

Autor: Saha, Indrashish, Gupta, Ashwini, Graham-Brady, Lori

Design and analysis of inelastic materials requires prediction of physical responses that evolve under loading. Numerical simulation of such behavior using finite element (FE) approaches can call for significant time and computational effort. To addr

Externí odkaz: http://arxiv.org/abs/2310.19128

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Accelerated multiscale mechanics modeling in a deep learning framework

Autor: Gupta, Ashwini, Bhaduri, Anindya, Graham-Brady, Lori

Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely, load distribution at the macro-scale changes the microstructural response. These up-scaling and down-scaling relations are often modeled using multiscale finite

Externí odkaz: http://arxiv.org/abs/2212.14601

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Stress field prediction in fiber-reinforced composite materials using a deep learning approach

Autor: Bhaduri, Anindya, Gupta, Ashwini, Graham-Brady, Lori

Computational stress analysis is an important step in the design of material systems. Finite element method (FEM) is a standard approach of performing stress analysis of complex material systems. A way to accelerate stress analysis is to replace FEM

Externí odkaz: http://arxiv.org/abs/2111.05271

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An efficient optimization based microstructure reconstruction approach with multiple loss functions

Autor: Bhaduri, Anindya, Gupta, Ashwini, Olivier, Audrey, Graham-Brady, Lori

Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures

Externí odkaz: http://arxiv.org/abs/2102.02407

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