9 citations · 9 across the 5 of their papers we have counts for
5 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…
Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
Murray Cutforth, Yiming Yang, Tiffany Fan +2
Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study eff…
Bi-fidelity Interpolative Decomposition for Multimodal Data
Murray Cutforth, Tiffany Fan, Tony Zahtila +2
Multi-fidelity simulation is a widely used strategy to reduce the computational cost of many-query numerical simulation tasks such as uncertainty quantification, design space explo…
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…
Solving Inverse Problems in Steady-State Navier-Stokes Equations using Deep Neural Networks
Tiffany Fan, Kailai Xu, Jay Pathak +1
Inverse problems in fluid dynamics are ubiquitous in science and engineering, with applications ranging from electronic cooling system design to ocean modeling. We propose a genera…