A Multi-Scale Deep Learning Framework for Projecting Weather Extremes

no code implementations • 21 Oct 2022 • Antoine Blanchard, Nishant Parashar, Boyko Dodov, Christian Lessig, Themistoklis Sapsis

Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year.

Modelling pressure-Hessian from local velocity gradients information in an incompressible turbulent flow field using deep neural networks

no code implementations • 19 Nov 2019 • Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan

The predictions made by the TBNN are tested against two different isotropic turbulence datasets at Reynolds number of 433 (JHTD) and 315 (UP Madrid turbulence database, UPMTD) and channel flow dataset at Reynolds number of 1000 (UT Texas and JHTD).

Distributed physics informed neural network for data-efficient solution to partial differential equations

no code implementations • 21 Jul 2019 • Vikas Dwivedi, Nishant Parashar, Balaji Srinivasan

The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations.