1.1k citations · 1.9k across the 12 of their papers we have counts for
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao, Luning Sun, Jian-Xun Wang
High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data a…
A deep-learning based generalized reduced-order model of glottal flow during normal phonation
Yang Zhang, Weili Jiang, Luning Sun +5
This paper proposes a deep-learning based generalized reduced-order model (ROM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The app…
PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
Han Gao, Luning Sun, Jian-Xun Wang
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDE…
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
Luning Sun, Jian-Xun Wang
In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is sig…