Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks

This content describes a Physics-Informed Neural Network (PINN) that fuses satellite sea surface temperature (SST) with sparse in-situ loggers to resolve depth-resolved coral reef thermal fields. The model effectively corrects for overestimations of subsurface thermal stress, achieving high accuracy even with minimal training data.