4 papers
Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
Tiffany Fan, Murray Cutforth, Marta D'Elia +3
Extracting compact, physically interpretable representations from high-dimensional scientific data is a persistent challenge due to the complex, nonlinear structures inherent in ph…
Physically Interpretable Representation and Controlled Generation for Turbulence Data
Tiffany Fan, Murray Cutforth, Marta D'Elia +3
Computational Fluid Dynamics (CFD) plays a pivotal role in fluid mechanics, enabling precise simulations of fluid behavior through partial differential equations (PDEs). However, t…
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan
In recent years, identification of nonlinear dynamical systems from data has become increasingly popular. Sparse regression approaches, such as Sparse Identification of Nonlinear D…
Sparse Identification of Nonlinear Dynamical Systems via Reweighted -regularized Least Squares
Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan
This work proposes an iterative sparse-regularized regression method to recover governing equations of nonlinear dynamical systems from noisy state measurements. The method is insp…