| A fast accurate fine-grain object detection model based on YOLOv4 deep neural network AM Roy, R Bose, J Bhaduri Neural Computing and Applications 34 (5), 3895-3921, 2022 | 185 | 2022 |
| Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity AM Roy, R Bose, V Sundararaghavan, R Arróyave Neural Networks 162, 472-489, 2023 | 26 | 2023 |
| Accurate deep learning sub-grid scale models for large eddy simulations R Bose, AM Roy arXiv preprint arXiv:2307.10060, 2023 | 15 | 2023 |
| Helical modes in boundary layer transition R Bose, PA Durbin Physical Review Fluids 1 (7), 073602, 2016 | 15 | 2016 |
| Transition to turbulence by interaction of free-stream and discrete mode perturbations R Bose, PA Durbin Physics of Fluids 28 (11), 114105, 2016 | 13 | 2016 |
| Transition to turbulence by interaction of free-stream and discrete mode perturbations PD Rikhi Bose APS Meeting Abstracts, 2014 | 13* | 2014 |
| Physics-aware deep learning framework for linear elasticity AM Roy, R Bose arXiv preprint arXiv:2302.09668, 2023 | 9 | 2023 |
| Instability waves and transition in adverse-pressure-gradient boundary layers R Bose, TA Zaki, PA Durbin Physical Review Fluids 3 (5), 053904, 2018 | 9 | 2018 |
| Direct numerical simulation of transitional mixed convection flows: Viscous and inviscid instability mechanisms TK Sengupta, S Bhaumik, R Bose Physics of Fluids 25 (9), 2013 | 9 | 2013 |
| A real time prediction methodology for hurricane evolution using LSTM recurrent neural networks R Bose, A Pintar, E Simiu Neural Computing and Applications 34 (20), 17491-17505, 2022 | 8 | 2022 |
| Analysis and design of a new dispersion relation preserving alternate direction bidiagonal compact scheme R Bose, TK Sengupta Journal of Scientific Computing 64, 55-82, 2015 | 8 | 2015 |
| Simulations of flow over an axisymmetric hill R Bose, D Yeo National Institute of Standards and Technology, https://nvlpubs. nist. gov …, 2021 | 6 | 2021 |
| Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows R Bose, AM Roy Engineering Applications of Artificial Intelligence 128, 107483, 2024 | 4 | 2024 |
| Forecasting the evolution of north atlantic hurricanes: A deep learning approach R Bose, AL Pintar, E Simiu Technical Note (NIST TN), National Institute of Standards and Technology …, 2021 | 4 | 2021 |
| Simulation of atlantic hurricane tracks and features: A coupled machine learning approach R Bose, AL Pintar, E Simiu Artificial Intelligence for the Earth Systems 2 (2), 220060, 2023 | 3 | 2023 |
| Simulation of atlantic hurricane tracks and features: A deep learning approach R Bose, AL Pintar, E Simiu arXiv preprint arXiv:2209.06901, 2022 | 3 | 2022 |
| Mixed mode transition to turbulence in boundary layers R Bose Iowa State University, 2016 | 3 | 2016 |
| Effect of inflow turbulence on separational flow over smooth-wall axisymmetric hill R Bose Computers & Fluids 251, 105762, 2023 | 2 | 2023 |
| Inflow turbulence effects on large eddy simulations of the flow around an axisymmetric hill R Bose, R Bose, E Simiu US Department of Commerce, National Institute of Standards and Technology, 2022 | 2 | 2022 |
| Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach R Bose, A Pintar, E Simiu arXiv preprint arXiv:2111.12683, 2021 | 1 | 2021 |
Arunabha M RoyTexas A&M ---- U of Michigan, Iowa StateVerified email at umich.edu
