1 citations · 1 across the 1 of their papers we have counts for
5 papers
Reduced-order modelling of parametrized unsteady Navier-Stokes equations and application to flow around cylinders with periodic changing boundary conditions
Shan Ding, Yongfu Tian, Rui Yang
Computational fluid dynamics (CFD) simulations play an important role in engineering science and applications, however, it is not applicable for problems requiring a large number o…
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs
Shan Ding, Yongfu Tian, Lang Qin +3
Driven by rapid advances in artificial intelligence and modern GPU computing capabilities, deep learning methods based on the optimization paradigm have provided new pathways to so…
A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method
Yongfu Tian, Shan Ding, Guofeng Su +1
Urban flood disaster is one of the most serious natural disasters. Numerous flood simulation models have been proposed and relatively matured. However, two major challenges persist…
Calibration of the underlying surface parameters for urban flood using latent variables and adjoint equation
Yongfu Tian, Shan Ding, Guofeng Su +1
Calibrating the urban underlying surface parameters is crucial for urban flood simulation. We formulate the parameter calibration problem into an optimization problem within the Ba…
Physics-Informed Neural Networks for Solving the Two-Dimensional Shallow Water Equations with Terrain Topography and Rainfall Source Terms
Yongfu Tian, Shan Ding, Guofeng Su +2
Solving the two-dimensional shallow water equations is a fundamental problem in flood simulation technology. In recent years, physics-informed neural networks (PINNs) have emerged…