activity
20222025
most citedEmbed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Hierarchical Implicit Neural Emulators

Ruoxi Jiang, Xiao Zhang, Karan Jakhar +4

Neural PDE solvers offer a powerful tool for modeling complex dynamical systems, but often struggle with error accumulation over long time horizons and maintaining stability and ph…

quant-ph2025

Towards efficient quantum algorithms for diffusion probabilistic models

Yunfei Wang, Ruoxi Jiang, Yingda Fan +4

A diffusion probabilistic model (DPM) is a generative model renowned for its ability to produce high-quality outputs in tasks such as image and audio generation. However, training…

cs.CV2024

Nested Diffusion Models Using Hierarchical Latent Priors

Xiao Zhang, Ruoxi Jiang, Rebecca Willett +1

We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particular…

cs.LG2024

Embed and Emulate: Contrastive representations for simulation-based inference

Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

Scientific modeling and engineering applications rely heavily on parameter estimation methods to fit physical models and calibrate numerical simulations using real-world measuremen…

cs.LG20221 cited

Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification

Ruoxi Jiang, Rebecca Willett

This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems. We assume access to a computationally complex sim…