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20232026
most citedConditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

physics.plasm-ph2026

A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling

Minglei Yang, Marshall Nicholson, Diego Del-Castillo-Negrete +2

Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pr…

math.NA2026

Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking

Siming Liang, Jacob Hauck, Minglei Yang +3

Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance…

cs.LG2025

Bifidelity Parameter Estimation Using Conditional Diffusion Models

Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1

We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distr…

cs.LG2024★ 2 cited

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

Minglei Yang, Pengjun Wang, Ming Fan +3

We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and i…

cs.LG2023

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Yanfang Liu, Minglei Yang, Zezhong Zhang +3

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, va…