Sparse-Observation Atmospheric Thermal Forecasting with Physics-Informed Neural Networks for Climate-Aware Digital Twins
CFD/Fluid
This study evaluates physics-informed neural networks for atmospheric potential-temperature forecasting, constrained by pressure-coordinate thermodynamic advection-source equations to address short-horizon predictions under sparse thermal observation conditions.
See LICENSE file Physics-Informed Neural NetworksAtmospheric Temperature ForecastingDigital Twins