24 citations · 24 across the 3 of their papers we have counts for
3 papers
Network Compression for Machine-Learnt Fluid Simulations
Peetak Mitra, Vaidehi Venkatesan, Nomit Jangid +7
Multi-scale, multi-fidelity numerical simulations form the pillar of scientific applications related to numerically modeling fluids. However, simulating the fluid behavior characte…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
An Analysis of the Convergence of Stochastic Lagrangian/Eulerian Spray Simulations
David P. Schmidt, Frederick Bedford
This work derives how the convergence of stochastic Lagrangian/Eulerian simulations depends on the number of computational parcels, particularly for the case of spray modeling. A n…