297 citations · 323 across the 8 of their papers we have counts for
4 papers · 1 filter
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson +2
Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We…
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez +5
We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differen…
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Zhong Yi Wan, Ricardo Baptista, Yi-fan Chen +4
We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data…
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems
Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral +1
We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations. Th…