5 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion
Yifei Guan, Pedram Hassanzadeh, Tapio Schneider +4
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and…
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang +2
The dynamics of probability density functions have been extensively studied in computational science and engineering to understand physical phenomena and facilitate algorithmic des…
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesi…
Convergence Analysis of Probability Flow ODE for Score-based Generative Models
Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinni…
An operator learning perspective on parameter-to-observable maps
Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering. Operator learning provides data-driven surrogates that map bet…