Computational Fluids and Aerodynamics Laboratory
← All research areas

Real-Time and AI-Accelerated Modeling

GPU-based CFD, lattice-Boltzmann methods, reduced-order models, machine learning, physics-informed models, and digital twins for near-real-time prediction.

Students & Researchers

SS

Ph.D. Candidate

Sweety Sarker

Machine learning and physics-informed neural network (PINN) models for real-time, micro-scale atmospheric flow prediction

AT

Ph.D. Candidate (M.S. to Ph.D. track)

Andres Torres

Lattice Boltzmann methods (LBM) for real-time CFD

Selected Publications

2026

Physics-Informed Neural Networks for Real-Time, Micro-scale Atmospheric Flow Prediction

S Sarker, B Cavainolo, M Kinzel

AIAA SCITECH 2026 Forum, 1933, 2026

2026

A Study of Physics-Informed Neural Networks applied to Real-Time Atmospheric Flow Estimation for Glider Flight Planning

S Sarker, BA Cavainolo, JJ Bird, M Berk, MP Kinzel

2025

Optimization of logistical models for rocket-based cargo delivery

KC Nguyen, M Elkamel, L Rabelo, MP Kinzel

AIAA SCITECH 2025 Forum, 1922, 2025

2025

Optimizing Bluff Vehicle Geometry With Machine Learning

ZA Miles, KC Nguyen, MP Kinzel

AIAA SCITECH 2025 Forum, 1925, 2025

2025

Physics-Informed Neural Networks for Predicting Steady Incompressible Flow Around Obstacles in Urban and Aerodynamic Settings

S Sarker, BA Cavainolo, M Kinzel

78th Annual Meeting of the Division of Fluid Dynamics, 2025

2025

Machine Learning-Driven Aerodynamic Optimization of Bluff Body Vehicle Geometry

ZA Miles, KC Nguyen, MP Kinzel

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025, 2025

2024

Data-driven modeling to predict aerodynamic loads on reentry vehicles

ZA Miles, M Kinzel, S Lopez

AIAA AVIATION FORUM AND ASCEND 2024, 4410, 2024

2024

Gaussian Process Regression Implemented as Surrogate Model to Aid Aerodynamic Design Process

ZA Miles, S Lopez, MP Kinzel

AIAA Aviation Forum and ASCEND, 2024, 2024