Ph.D. Candidate
Sweety Sarker
Machine learning and physics-informed neural network (PINN) models for real-time, micro-scale atmospheric flow prediction
GPU-based CFD, lattice-Boltzmann methods, reduced-order models, machine learning, physics-informed models, and digital twins for near-real-time prediction.
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)
Real-time simulation methods for predicting helicopter flight dynamics operating within the turbulent airwake behind a ship superstructure, supporting safer shipboard rotorcraft operations.
Navy (Phase I SBIR)
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
S Sarker, BA Cavainolo, JJ Bird, M Berk, MP Kinzel
ZA Miles, KC Nguyen, MP Kinzel
AIAA SCITECH 2025 Forum, 1925, 2025
KC Nguyen, M Elkamel, L Rabelo, MP Kinzel
AIAA SCITECH 2025 Forum, 1922, 2025
ZA Miles, KC Nguyen, MP Kinzel
AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025, 2025
S Sarker, BA Cavainolo, M Kinzel
78th Annual Meeting of the Division of Fluid Dynamics, 2025
ZA Miles, M Kinzel, S Lopez
AIAA AVIATION FORUM AND ASCEND 2024, 4410, 2024
ZA Miles, S Lopez, MP Kinzel
AIAA Aviation Forum and ASCEND, 2024, 2024