24 citations · 34 across the 7 of their papers we have counts for
7 papers
Improving CFD simulations by local machine-learned correction
Peetak Mitra, Majid Haghshenas, Niccolo Dal Santo +2
High-fidelity computational fluid dynamics (CFD) simulations for design space explorations can be exceedingly expensive due to the cost associated with resolving the finer scales.…
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
Varun Shankar, Gavin D. Portwood, Arvind T. Mohan +6
In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as alternatives to turbulence cl…
Improved Methods for Mixing-Limited Spray Modeling
Majid Haghshenas, Peetak P. Mitra, Chu Wang +3
The realization that interfacial features play little role in diesel spray vaporization and advection has changed the modus operandi for spray modeling. Lagrangian particle trackin…
The Eulerian Lagrangian Mixing-Oriented (ELMO) Model
David P. Schmidt, Majid Haghshenas, Peetak P. Mitra +4
Past Lagrangian/Eulerian modeling has served as a poor match for the mixing limited physics present in many sprays. Though these Lagrangian/Eulerian methods are popular for their l…
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…