Partial Physics Informed Diffusion Model for Ocean Chlorophyll Concentration Reconstruction
Qianxun Xu, Zuchuan Li
The integration of big data, physical laws, and machine learning algorithms has shown potential to improve the estimation and understanding of complex real-world systems. However, integrating physical laws with uncertainties in machine learning algorithms remains understudied. In this work, we bridge this gap through developing the Partial-Physics Informed Diffusion Model (PPIDM), a novel framework that integrates known physical principles through a physics operator while reducing the impact of unknown dynamics by minimizing related discrepancies. We showcase PPIDM’s capabilities using surface chlorophyll concentration data, which is influenced by both physical and biological processes, while the latter is poorly constrained. Experimental results reveal that PPIDM achieves substantially improved prediction accuracy and stability, significantly outperforming baseline methods that either neglect physics entirely or misapply incomplete physical laws under an assumption of completeness.