248 citations · 379 across the 4 of their papers we have counts for
6 papers
Neural network scaling and generalization for 3D strain fields in heterogeneous materials
Lucas Fourel, Paul Devianne, Marine Debouvry +5
Computational homogenization and coupled multiscale simulations of heterogeneous materials require repeated solution of mechanical fields on Representative Volume Elements, which b…
Beyond Score Accuracy: Examining the Diagnostic Quality of LLM-Generated Structured Assessment in Higher Education
Xi Zhao, Xinyue Jiao, Zhen Xu
As Large Language Models (LLMs) are increasingly adopted for automated grading and feedback in higher education, their structured outputs, including multi-dimensional rubric scores…
Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network
Indrashish Saha, Ashwini Gupta, Lori Graham-Brady
Design and analysis of inelastic materials requires prediction of physical responses that evolve under loading. Numerical simulation of such behavior using finite element (FE) appr…
Accelerated multiscale mechanics modeling in a deep learning framework
Ashwini Gupta, Anindya Bhaduri, Lori Graham-Brady
Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely, load distribution at the macro-scale changes the microstructural response. These up-scaling…
Stress field prediction in fiber-reinforced composite materials using a deep learning approach
Anindya Bhaduri, Ashwini Gupta, Lori Graham-Brady
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 ma…
An efficient optimization based microstructure reconstruction approach with multiple loss functions
Anindya Bhaduri, Ashwini Gupta, Audrey Olivier +1
Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This p…