8 papers
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…
Rapid Hubble constant inference from GW170817 using GPU-accelerated nested sampling: prior sensitivity and the limits of post-hoc reweighting
Ming Han Yang, Metha Prathaban, David Yallup +1
The bright-siren measurement of the Hubble constant from GW170817 (Abbott et al. 2017) assumes that switching from a volumetric to a uniform-in- luminosity-distance prior can…
Training-free score-based diffusion for parameter-dependent stochastic dynamical systems
Minglei Yang, Sicheng He
Simulating parameter-dependent stochastic differential equations (SDEs) presents significant computational challenges, as separate high-fidelity simulations are typically required…
Error estimates of a training-free diffusion model for high-dimensional sampling
Pengjun Wang, Zezhong Zhang, Minglei Yang +3
Score-based diffusion models are a powerful class of generative models, but their practical use often depends on training neural networks to approximate the score function. Trainin…
An efficient probabilistic scheme for the exit time probability of -stable Lévy process
Minglei Yang, Diego del-Castillo-Negrete, Guannan Zhang
The α-stable Lévy process, commonly used to describe Lévy flight, is characterized by discontinuous jumps and is widely used to model anomalous transport phenomena. In this stud…
A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
Pei-Zhi Zhuang, Ming-Yue Yang, Fei Ren +2
The inverse Stefan problem, as a typical phase-change problem with moving boundaries, finds extensive applications in science and engineering. Recent years have seen the applicatio…