A single trained parameterized NA-PINN coupled to FDM delivers low-error solutions for gravity-driven draining across multiple time steps and initial conditions without retraining or simulation data.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
A single trained parameterized NA-PINN coupled to FDM delivers low-error solutions for gravity-driven draining across multiple time steps and initial conditions without retraining or simulation data.