Wesselkamp Marieke, Moser Niklas, Kalweit Maria, Boedecker Joschka, Dormann Carsten F
Biometry and Environmental System Analysis, University of Freiburg, Freiburg im Breisgau, Germany.
Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland.
Ecol Lett. 2024 Nov;27(11):e70012. doi: 10.1111/ele.70012.
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.
尽管深度学习是数据驱动模型预测的先进技术，但其在生态学中的应用目前受到两个重要限制：（i）深度学习方法在数据丰富的情况下很强大，但在生态学中数据通常很稀疏；（ii）深度学习模型是黑箱方法，推断它们所代表的过程并非易事。基于过程（=机制）的模型不受数据稀疏性或不明确过程的限制，因此对于积累我们的生态知识并应用于实际非常重要。在这项工作中，我们将基于过程的模型和神经网络结合成过程信息神经网络（PINN），它将过程知识直接纳入神经网络结构。在对温带森林碳通量的空间和时间预测任务进行系统评估时，我们展示了五种不同类型的PINN的能力：（i）优于基于过程的模型和神经网络，特别是在具有高转移任务的数据稀疏情况下；（ii）揭示错误或未检测到的过程。
