Distributed physics informed neural network for data-efficient solution to partial differential equations. - Vikas Dwivedi; Nishant Parashar; Balaji Srinivasan (CoRR, 2019)

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 (CoRR, 2019)

Distributed learning machines for solving forward and inverse problems in partial differential equations. - Vikas Dwivedi; Nishant Parashar; Balaji Srinivasan (Neurocomputing, 2021)

A Multi-Scale Deep Learning Framework for Projecting Weather Extremes. - Antoine Blanchard; Nishant Parashar; Boyko A. Dodov; Christian Lessig; Themistoklis P. Sapsis (CoRR, 2022)

TAUDiff: Improving statistical downscaling for extreme weather events using generative diffusion models. - Rahul Sundar; Nishant Parashar; Antoine Blanchard; Boyko A. Dodov (CoRR, 2024)

Continuous latent representations for modeling precipitation with deep learning. - Gokul Radhakrishnan; Rahul Sundar; Nishant Parashar; Antoine Blanchard; Daiwei Wang; Boyko A. Dodov (CoRR, 2024)