118 citations · 140 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations
Vikas Dwivedi, Balaji Srinivasan
There has been rapid progress recently on the application of deep networks to the solution of partial differential equations, collectively labelled as Physics Informed Neural Netwo…