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

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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…