248 citations · 368 across the 9 of their papers we have counts for
10 papers
AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments
Todd C. Hufnagel, Pranav Addepalli, Anuruddha Bhattacharjee +14
Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-…
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…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
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…
Probabilistic modeling of discrete structural response with application to composite plate penetration models
Anindya Bhaduri, Christopher S. Meyer, John W. Gillespie +3
Discrete response of structures is often a key probabilistic quantity of interest. For example, one may need to identify the probability of a binary event, such as, whether a struc…
Predicting high rate granular transition and fragment statistics at the onset of granular flow for brittle ceramics
Amartya Bhattacharjee, Ryan C. Hurley, Lori Graham-Brady
Brittle materials under impact loading exhibit a transition from a cracked solid to a granular medium. Appropriate representation of this transition to granular mechanics and the r…
A constitutive model for brittle granular materials considering the competition between breakage and dilation
M. B. Cil, R. C. Hurley, L. Graham-Brady
A constitutive model is presented for brittle granular materials based on a recent reformulation of the breakage mechanics theory. Compared with previous breakage mechanics-based m…
Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis
Anindya Bhaduri, Yanyan He, Michael D. Shields +2
Presence of a high-dimensional stochastic parameter space with discontinuities poses major computational challenges in analyzing and quantifying the effects of the uncertainties in…
An efficient adaptive sparse grid collocation method through derivative estimation
Anindya Bhaduri, Lori Graham-Brady
For uncertainty propagation of highly complex and/or nonlinear problems, one must resort to sample-based non-intrusive approaches [1]. In such cases, minimizing the number of funct…