Ph.D. Candidate
Sweety Sarker
Machine learning and physics-informed neural network (PINN) models for real-time, micro-scale atmospheric flow prediction
Atmospheric boundary layers, winds around buildings and terrain, wind-energy applications, and vehicle-environment interaction.
Real-time and near-real-time CFD-based prediction of atmospheric flight trajectories, supporting rapid decision-making for aerospace systems operating in complex, evolving atmospheric conditions.
DARPA (subcontract through Mississippi State University)
Numerical and experimental study of airborne pathogen transmission via saliva droplets and sneezing, including classroom social-distancing guidance and mitigation strategies, spanning three related awards.
NSF; UCF/ORC Florida High Tech Corridor Council; International Association of Amusement Parks and Attractions (IAAPA)
High-fidelity simulation of ship airwakes modified by atmospheric boundary-layer turbulence and their effect on helicopter flight dynamics during shipboard operations.
National Rotorcraft Technology Center (NRTC)
Large-eddy simulation study of wind turbine blade and shaft loading driven by atmospheric boundary-layer turbulence structure.
NSF
Ph.D. Candidate
Machine learning and physics-informed neural network (PINN) models for real-time, micro-scale atmospheric flow prediction
S Sarker, B Cavainolo, M Kinzel
AIAA SCITECH 2026 Forum, 1933, 2026
J Asiatico, M Kinzel
AJ Aguilera, SM Briggs, M Lindsay, B Suarez, S Vasu, M Kinzel, et al.
AIAA SCITECH 2026 Forum, 0001, 2026
B Cavainolo, S Sarker, MP Kinzel, JJ Bird, J Dyer, M Berk
M Marques, J Asiatico, R Lorenz, M Kinzel
S Sarker, BA Cavainolo, JJ Bird, M Berk, MP Kinzel
C Anderson, MP Kinzel, AJ Brune
AIAA SCITECH 2025 Forum, 1038, 2025
S Sarker, BA Cavainolo, M Kinzel
78th Annual Meeting of the Division of Fluid Dynamics, 2025