1.1k citations · 2.3k across the 10 of their papers we have counts for
10 papers
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
Han Gao, Xu Han, Xiantao Fan +4
Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources…
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations
Han Gao, Xu Han, Jiaoyang Huang +2
Recently the Transformer structure has shown good performances in graph learning tasks. However, these Transformer models directly work on graph nodes and may have difficulties lea…
Predicting Physics in Mesh-reduced Space with Temporal Attention
Xu Han, Han Gao, Tobias Pfaff +2
Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short te…
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Han Gao, Matthew J. Zahr, Jian-Xun Wang
Despite the great promise of the physics-informed neural networks (PINNs) in solving forward and inverse problems, several technical challenges are present as roadblocks for more c…
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…
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…
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…
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
Han Gao, Jian-Xun Wang, Matthew J. Zahr
Projection-based model reduction has become a popular approach to reduce the cost associated with integrating large-scale dynamical systems so they can be used in many-query settin…
A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations
Han Gao, Xueyu Zhu, Jian-Xun Wang
Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information, which has become a paradigm in cardiovascular research and healthcare. Noneth…
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
Luning Sun, Han Gao, Shaowu Pan +1
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system…