118 citations · 130 across the 5 of their papers we have counts for
5 papers
Continuous latent representations for modeling precipitation with deep learning
Gokul Radhakrishnan, Rahul Sundar, Nishant Parashar +3
The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downsc…
TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models
Rahul Sundar, Yucong Hu, Nishant Parashar +2
Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enabl…
A Multi-Scale Deep Learning Framework for Projecting Weather Extremes
Antoine Blanchard, Nishant Parashar, Boyko Dodov +2
Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and…
Modelling pressure-Hessian from local velocity gradients information in an incompressible turbulent flow field using deep neural networks
Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan
The understanding of the dynamics of the velocity gradients in turbulent flows is critical to understanding various non-linear turbulent processes. The pressure-Hessian and the vis…
Distributed physics informed neural network for data-efficient solution to partial differential equations
Vikas Dwivedi, Nishant Parashar, Balaji Srinivasan
The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations. In the recent past PINNs have been successfully tested and valida…