1.1k citations · 2.3k across the 10 of their papers we have counts for
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physics.flu-dyn2020★ 192 cited
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…
eess.IV2020★ 636 cited
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…
math.NA2020★ 20 cited
A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems
Han Gao, Jian-Xun Wang
Mathematical modeling and simulation of complex physical systems based on partial differential equations (PDEs) have been widely used in engineering and industrial applications. To…