24 citations · 24 across the 5 of their papers we have counts for
6 papers
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…
Modeling PKT at a global level: A machine learning approach
Peetak Mitra, Suhrid Deshmukh
It is well-accepted that the ability to go from one place to another, or mobility, contributes significantly to one's wellbeing. The need for mobility is universal, but the demand…
Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework
Peetak Mitra
We propose a new deep learning based framework to identify pedestrians, and caution distracted drivers, in an effort to prevent the loss of life and property. This framework uses t…