| Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images R Perera, D Guzzetti, V Agrawal Computational Materials Science 196, 110524, 2021 | 32 | 2021 |
| Graph neural networks for simulating crack coalescence and propagation in brittle materials R Perera, D Guzzetti, V Agrawal Computer Methods in Applied Mechanics and Engineering 395, 115021, 2022 | 30 | 2022 |
| A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks R Perera, V Agrawal Mechanics of Materials 181, 104639, 2023 | 8 | 2023 |
| Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models R Perera, V Agrawal Mechanics of Materials 186, 104789, 2023 | 6 | 2023 |
| Shedding some light on Light Up with Artificial Intelligence L Sun, J Browning, R Perera arXiv preprint arXiv:2107.10429, 2021 | 2 | 2021 |
| Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations R Perera, V Agrawal arXiv preprint arXiv:2402.08863, 2024 | 1 | 2024 |
| Predicting critical impact velocity in PBX-9501 using machine learning R Perera, B Mccracken, N Cummock, V Agrawal Bulletin of the American Physical Society 68, 2023 | 1 | 2023 |
| Development and applications of machine learning frameworks for dynamic emulation of aerospace multiphysics problems and characterization of microstructure R Perera | | 2024 |
Vinamra AgrawalAssistant Professor, Auburn UniversityVerified email at auburn.edu
