用于预测一氧化二氮通量的物理信息神经网络
Physics-Informed Neural Networks for Predicting Nitrous Oxide Flux
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中文总结 AI 辅助
研究针对一氧化二氮通量预测，利用基于DayCent模型的机理方程构建物理残差，通过在多站点农业数据集上训练基于MLP的PINN，发现PINN性能优于未校准的Cycles模拟，物理约束可提升模型性能及降低变异性，但跨站点泛化存挑战。
中文摘要 AI 辅助
一氧化二氮（N$_2$O）是21世纪排放的主要消耗臭氧层物质，也是人为温室气体的第三大贡献者，70%以上的N$_2$O排放源于农业过程。当前预测N$_2$O通量排放的方法包括基于过程的模型和经典AI模型，而物理信息神经网络（PINNs）在此方面的应用探索较少。本文利用基于过程的DayCent模型的机理方程构建物理残差，在多站点农业数据集上构建并训练基于MLP的PINN。结果表明，PINN在所有测试的物理损失加权超参数$\lambda$值下均优于未校准的Cycles模拟，物理约束在留一法验证中提高了模型性能并降低了性能变异性，但跨站点泛化仍具有挑战性。
英文摘要
Nitrous oxide (N$_2$O) is the dominant ozone-depleting substance emitted in the 21st century, and the third largest contributor to anthropogenic greenhouse gases due to its high potency and long atmospheric lifetime, with more than 70% of N$_2$O emissions occurring as a result of agricultural processes. Current approaches to predicting N$_2$O flux emissions include process-based models such as DayCent and Cycles, as well as classical AI models, but the application of Physics-Informed Neural Networks (PINNs) to predicting N$_2$O flux emissions is largely underexplored. Our paper draws upon the mechanistic equations that underlie the DayCent family of process-based models to construct a rigorously derived, literature-traceable physics residual. We then build and train an MLP-based PINN on a multi-site agricultural dataset spanning four geographically distinct US agricultural sites. Across all tested values of the physics loss weighting hyperparameter $λ$, our PINN consistently and substantially outperformed uncalibrated Cycles simulation (R$^2=0.01$), with our MLP baseline achieving mean R$^2=0.411$ across ten random seeds. Physics constraints consistently degrade model performance in holdout validation, with marginal degradation at low $λ$ and significant degradation at high $λ$, but consistently improve model performance and reduce performance variability in leave-one-site-out validation. This suggests that physics constraints sacrifice in-distribution accuracy for out-of-distribution robustness, anchoring the model toward biogeochemically plausible behavior on unfamiliar soil conditions --- though cross-site generalization remains challenging, with negative R$^2$ across all seeds and $λ$ values on our geographically distinct held-out site.
发表机构
- Harvard University（哈佛大学）
- University of Tennessee（田纳西大学）
机构由 AI 辅助整理，请以论文原文为准。
