Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method
arXiv:2410.20203 · doi:10.1016/j.expthermflusci.2025.111562
Abstract
This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks
arXiv commentExperimental Thermal and Fluid Science (2025)
References in corpus (5)
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
- Flow over an espresso cup: Inferring 3D velocity and pressure fields from tomographic background oriented schlieren videos via physics-informed neural networks
- PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
- A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
- High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features