Coupling Intuitive Physics Into Deep Learning for Soil Moisture Flow Processes Learning

Abstract Soil water flow processes in the unsaturated zone support ecosystems and regulate water, energy, and biogeochemical cycles. Recently, deep learning (DL) approaches have significantly advanced soil moisture (SM) prediction tasks yet still challenging to interpret. It's difficult to peer into the internal reasoning procedures of algorithms, let alone associate them with specific physical processes. Thus, DL alone is unlikely to satisfy soil hydrological modeling needs and cannot advance process understanding. Here, we present DPL‐S (deep process learning for SM dynamics) approach, which couples intuitive physics into deep learning architecture as structural guidance to facilitate comprehensive surrogate modeling of soil water flow. DPL‐S discretizes the SM state evolution into multiple sub‐process effects (e.g., gravity, matric potential) at the intuitive physics level and abstracts them into format‐specific and learnable tensors. By cascading state‐action matrices in a differentiable end‐to‐end framework and enforcing penalties for physical inconsistencies, DPL‐S enables a profound understanding of physical functions and scenes of soil water flow. Comprehensive numerical experiments including layered soil conditions and tests with in situ observations, demonstrate that it achieves reliable SM profile reconstruction with predictive performance comparable to the state‐of‐the‐art DL model on supervised items. The internal inference of DPL‐S is fully transparent and the tensor representations achieve strict physical realism under limited water content supervision, thus enabling continuous predictions like physical models during the testing period. The model's flexibility, generalization, noise resistance, and large‐sample diverse data synergies are also evaluated. This work represents a solid step toward learning hydrophysical processes from large data sets.