most citedPhysics-Informed Neural Networks with Complementary Soft and Hard Constraints for Solving Complex Boundary Navier-Stokes Equations

3 citations · 3 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CV2026

DTurb: Depth-Aware Simulation and Decoupled Learning for Single-Frame Atmospheric Turbulence Mitigation

Zixiao Hu, Tianyu Li, Guoqing Wang +4

Single-frame atmospheric turbulence mitigation is inherently ill-posed due to spatially varying blur coupled with non-rigid geometric distortion. Existing end-to-end approaches tra…

cs.LG2026

DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting

Yeran Zhang, Pengwei Yang, Guoqing Wang +1

Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operating conditions. Existing data-driven mod…

physics.flu-dyn2024★ 3 cited

Physics-Informed Neural Networks with Complementary Soft and Hard Constraints for Solving Complex Boundary Navier-Stokes Equations

Chuyu Zhou, Tianyu Li, Chenxi Lan +7

Soft- and hard-constrained Physics Informed Neural Networks (PINNs) have achieved great success in solving partial differential equations (PDEs). However, these methods still face…