Abstract
Despite deep learning being state of the art for data‐driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep‐learning methods are powerful in data‐rich regimes, but in ecology data are typically sparse; and (ii) deep‐learning models are black‐box methods and inferring the processes they represent are non‐trivial to elicit. Process‐based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process‐based models and neural networks into process‐informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C‐fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process‐based models and neural networks, especially in data‐sparse regimes with high‐transfer task and (ii) to inform on mis‐ or undetected processes.