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
KC Nguyen, M Elkamel, L Rabelo, MP Kinzel
AIAA SCITECH 2025 Forum, 1922, 2025
ZA Miles, KC Nguyen, MP Kinzel
AIAA SCITECH 2025 Forum, 1925, 2025
S Sarker, BA Cavainolo, M Kinzel
78th Annual Meeting of the Division of Fluid Dynamics, 2025
ZA Miles, KC Nguyen, MP Kinzel
AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025, 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