1.2k citations · 1.5k across the 15 of their papers we have counts for
15 papers
In-Context Operator Learning with Data Prompts for Differential Equation Problems
Liu Yang, Siting Liu, Tingwei Meng +1
This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new…
Learning Functional Priors and Posteriors from Data and Physics
Xuhui Meng, Liu Yang, Zhiping Mao +2
We develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both nois…
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates
Liu Yang, Tingwei Meng, George Em Karniadakis
We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs. By taking the generat…
Flow-based Generative Models for Learning Manifold to Manifold Mappings
Xingjian Zhen, Rudrasis Chakraborty, Liu Yang +1
Many measurements or observations in computer vision and machine learning manifest as non-Euclidean data. While recent proposals (like spherical CNN) have extended a number of deep…
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks
Xiaoli Chen, Liu Yang, Jinqiao Duan +1
The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficient…
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models
Liu Yang, Constantinos Daskalakis, George Em Karniadakis
We propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., m…