Computational Flow Physics Group
University of California San Diego
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We develop advanced numerical simulation and data-driven analysis tools to understand, model, and predict turbulent and multiphysics flows in engineering and nature. Our work combines high-fidelity computation with modal decomposition and feature extraction to reveal coherent flow structures and translate them into predictive reduced-order models for forecasting and optimization. We focus on aerospace problems, including jet noise control, unsteady aerodynamics and aeroacoustics, transition, dynamic stall, aero-optics, and hypersonics. While our work is grounded in physics-based modeling, we also curiously explore where modern machine learning can complement first-principles simulation, statistical methods, and classical closure techniques. Beyond aerospace, our methods extend to complex geophysical flows, with applications in atmospheric science and physical oceanography.
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Enviada
Triadic Orthogonal Decomposition
MATLAB implementation of Triadic Orthogonal Decomposition (TOD)
12 días hace | 6 descargas |
Enviada
SLICK: Data-driven prediction of turbulent flows
Stochastic Low-Dimensional Inflated Convolutional Koopman model (Chu & Schmidt, PRSA 2025). Code developed by Tianyi Chu.
4 meses hace | 1 descarga |
Enviada
Spectral proper orthogonal decomposition (SPOD)
Implements the frequency domain form of proper orthogonal decomposition (POD)
8 meses hace | 16 descargas |
Enviada
LinStab2D
Stability and resolvent analysis of compressible viscous flows in MATLAB
más de 1 año hace | 13 descargas |
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Spectral EOF (SEOF) of weather and climate data
2-D and 3-D spectral empirical orthogonal function analysis (SEOF) of ECMWF reanalysis data
más de 6 años hace | 2 descargas |
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Streaming Spectral Proper Orthogonal Decomposition
A low-memory streaming algorithm for spectral proper orthogonal decomposition (SPOD) of stationary random data
más de 7 años hace | 2 descargas |
