Multifidelity linear regression for scientific machine learning from scarce data
July 3, 2024
Proposed a multifidelity linear regression approach that significantly reduces model variance and improves robustness in data-scarce scenarios.
Read More
Projection-based multifidelity linear regression for data-poor applications
January 19, 2023
Developed multifidelity linear regression methods to enhance predictive accuracy in data-poor, high-dimensional applications, showing significant improvement on a hypersonic vehicle surface pressure prediction example.
Read More
Improving Neural Network Efficiency With Multifidelity and Dimensionality Reduction Techniques
January 10, 2022
Developed projection-enabled multifidelity neural networks to reduce computational costs for a 2D aerodynamic airfoil inverse design problem.
Read More
Turbomachinery Blade Surrogate Modeling using Deep Learning
November 13, 2021
Leveraging convolutional neural networks for rapid and efficient aerodynamic performance evaluation, offering a faster alternative to traditional CFD solvers in turbo-machinery blade design in the early design cycle.
Read More
Development of a nytrox-paraffin hybrid rocket engine
August 17, 2020
Student-led project to develop a hybrid rocket engine using a novel Nytrox-paraffin oxidizer and fuel combination. (Featured on the news!)
Read More