16 citations · 29 across the 5 of their papers we have counts for
7 papers
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…
Reinforcement Learning for Active Flow Control in Experiments
Dixia Fan, Liu Yang, Michael S Triantafyllou +1
We demonstrate experimentally the feasibility of applying reinforcement learning (RL) in flow control problems by automatically discovering active control strategies without any pr…
An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging
Liu Yang, Rudrasis Chakraborty
Recent years have witnessed the emergence and increasing popularity of 3D medical imaging techniques with the development of 3D sensors and technology. However, achieving geometric…
A GMM based algorithm to generate point-cloud and its application to neuroimaging
Liu Yang, Rudrasis Chakraborty
Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, reg…
POIRot: A rotation invariant omni-directional pointnet
Liu Yang, Rudrasis Chakraborty, Stella X. Yu
Point-cloud is an efficient way to represent 3D world. Analysis of point-cloud deals with understanding the underlying 3D geometric structure. But due to the lack of smooth topolog…
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling…