Stress field prediction in fiber-reinforced composite materials using a deep learning approach A Bhaduri, A Gupta, L Graham-Brady Composites Part B: Engineering 238, 109879, 2022 | 107 | 2022 |
An efficient optimization based microstructure reconstruction approach with multiple loss functions A Bhaduri, A Gupta, A Olivier, L Graham-Brady Computational Materials Science 199, 110709, 2021 | 32 | 2021 |
Accelerated multiscale mechanics modeling in a deep learning framework A Gupta, A Bhaduri, L Graham-Brady Mechanics of Materials, 104709, 2023 | 20 | 2023 |
Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network I Saha, A Gupta, L Graham-Brady Computer Methods in Applied Mechanics and Engineering 421, 116816, 2024 | 2 | 2024 |
Reconstruction of 3D microstructures from 2D images using a gradient-based sequential optimization approach A Gupta, N Wade, L Graham-Brady MACH 2023, 2023 | | 2023 |
End-to-end deep learning method to predict stress tensor field for composites with application in multiscaling A Gupta, A Bhaduri, L Graham-Brady Engineering Mechanics Institute (EMI 2022), 2022 | | 2022 |
Deep learning model to predict stress tensor field in fiber-reinforced composite materials with application in multiscale materials modeling A Gupta, A Bhaduri, L Graham-Brady MACH 2022, 2022 | | 2022 |
Microstructure reconstruction using a transfer learning approach and structure-property studies with applications to porous materials A Gupta, A Bhaduri, L Graham-Brady The Biot-Bazant Conference, 2021 | | 2021 |
Reconstruction of 2D and 3D microstructures using transfer learning with applications to porous materials A Gupta, A Bhaduri, L Graham-Brady MACH 2021, 2021 | | 2021 |
2D and 3D microstructure reconstruction using a transfer learning approach and structure-property studies A Gupta, A Bhaduri, L Graham-Brady 16th U.S. National Congress on Computational Mechanics, 2021 | | 2021 |