10 citations · 10 across the 2 of their papers we have counts for
3 papers
physics.flu-dyn2025
Physics-Informed Neural Networks for the Korteweg-de Vries Equation for Internal Solitary Wave Problem: Forward Simulation and Inverse Parameter Estimation
Ming Kang, Hang Li, Qiwen Tan +5
Physics-informed neural networks (PINNs) have emerged as a transformative framework for addressing operator learning and inverse problems involving the Korteweg-de Vries (KdV) equa…
physics.flu-dyn2024
AeroDiT: Diffusion Transformers for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Chunyang Wang, Biyue Pan, Zhibo Dai +5
Real-time and accurate prediction of aerodynamic flow fields around airfoils is crucial for flow control and aerodynamic optimization. However, achieving this remains challenging d…
physics.flu-dyn2023★ 10 cited
Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks
Zhicheng Wang, Xuhui Meng, Xiaomo Jiang +2
Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for fluid flows and are especially effective for inverse and hybrid problems. However, vanilla PI…