| Distributed learning machines for solving forward and inverse problems in partial differential equations V Dwivedi, N Parashar, B Srinivasan Neurocomputing 420, 299-316, 2020 | 60 | 2020 |
| Distributed physics informed neural network for data-efficient solution to partial differential equations V Dwivedi, N Parashar, B Srinivasan arXiv preprint arXiv:1907.08967, 2019 | 44 | 2019 |
| Modeling the pressure-Hessian tensor using deep neural networks N Parashar, B Srinivasan, SS Sinha Physical Review Fluids 5 (11), 114604, 2020 | 17 | 2020 |
| Lagrangian investigations of vorticity dynamics in compressible turbulence N Parashar, SS Sinha, M Danish, B Srinivasan Physics of Fluids 29 (10), 2017 | 13 | 2017 |
| A multi-scale deep learning framework for projecting weather extremes A Blanchard, N Parashar, B Dodov, C Lessig, T Sapsis arXiv preprint arXiv:2210.12137, 2022 | 11 | 2022 |
| Lagrangian investigations of velocity gradients in compressible turbulence: lifetime of flow-field topologies N Parashar, SS Sinha, B Srinivasan Journal of Fluid Mechanics 872, 492-514, 2019 | 9 | 2019 |
| GPU‐accelerated direct numerical simulations of decaying compressible turbulence employing a GKM‐based solver N Parashar, B Srinivasan, SS Sinha, M Agarwal International Journal for Numerical Methods in Fluids 83 (10), 737-754, 2017 | 9 | 2017 |
| Lagrangian evaluations of viscous models for velocity gradient dynamics in non-stationary turbulence N Parashar, SS Sinha, B Srinivasan International Journal of Heat and Fluid Flow 78, 108429, 2019 | 2 | 2019 |
| Reducing Biases and Improving Resolution of General Circulation Models with Statistically Consistent Machine Learning A Blanchard, N Parashar, B Dodov, C Lessig, T Sapsis AGU Fall Meeting Abstracts 2022, GC22I-0685, 2022 | | 2022 |
| Investigating small scale turbulence processes in compressible flows using gas kinetic method and machine learning N Parashar Delhi, 2020 | | 2020 |
| Modelling pressure-Hessian from local velocity gradients information in an incompressible turbulent flow field using deep neural networks N Parashar, SS Sinha, B Srinivasan arXiv preprint arXiv:1911.08056, 2019 | | 2019 |
| Influence of compressibility on lifetimes of topologies in turbulent flows S Sinha, N Parashar, B Srinivasan APS Division of Fluid Dynamics Meeting Abstracts, Q18. 003, 2019 | | 2019 |
| Investigating the dynamics of vorticity and strain rate in compressible turbulent flows N Parashar, SS Sinha, M Danish, B Srinivasan APS Division of Fluid Dynamics Meeting Abstracts, Q18. 002, 2019 | | 2019 |
| Evaluation of Cubic-Spline Based Gas Kinetic Method for Simulating Compressible Turbulence N Parashar, B Srinivasan, SS Sinha Journal of Applied Fluid Mechanics 12 (5), 1697-1706, 2019 | | 2019 |
| Evaluation of the Gas Kinetic Scheme Based on Analytical Solution to the BGK Model for Simulating Compressible Turbulent Flows N Parashar, B Srinivasan, SS Sinha Fluid Mechanics and Fluid Power–Contemporary Research, 615-623, 2017 | | 2017 |
| Tracking Lagrangian Quantities in DNS of Isotropic Decaying Turbulence M Danish, N Parashar, SS Sinha, B Srinivasan 16th Annual CFD Symposium, August 11-12 , 2014, Bangalore, 2014 | | 2014 |
Balaji SrinivasanProfessor, Mechanical Engineering, IIT MadrasCorreu electrònic verificat a iitm.ac.in
