A coupled PINN framework reconstructs greenhouse temperature and humidity dynamics and identifies physical parameters from limited observations, outperforming data-driven baselines on synthetic diurnal benchmarks.
Physics-informed machine learning
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Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics
A coupled PINN framework reconstructs greenhouse temperature and humidity dynamics and identifies physical parameters from limited observations, outperforming data-driven baselines on synthetic diurnal benchmarks.