FNO-MoE：主动训练的神经算子实现近乎显式的大气化学模拟
An actively trained neural operator for near-explicit atmospheric chemistry
AI 导读
研究者提出 FNO-MoE，一种结合混合专家的 Fourier neural operator，可单次调用预测 30 分钟内完整近乎显式化学状态（如 Master Chemical Mechanism）。该网络利用专家间分歧主动采样最有信息量的 box-model 模拟，比随机或均匀采样更精确；在长轨迹上微调后可稳定运行多小时，对未见化学成分保持准确，且比数值求解器快数个量级。
正文 · 原文
Abstract:Earth-system models simplify atmospheric chemistry into condensed mechanisms, because near-explicit mechanisms such as the Master Chemical Mechanism (MCM) are too costly to solve in every grid cell. Neural emulators could lift this limit, but most advance the chemistry in small time steps and cover only a few species or a reduced mechanism. Here we introduce FNO-MoE, a Fourier neural operator with a mixture of experts that predicts the full near-explicit chemical state over 30 minutes in a single call. The same network also guides its own training, using the disagreement among its experts to select the most informative box-model simulations. At equal cost, this active sampling yields a more accurate emulator than random or evenly spread sampling. Fine-tuning on longer trajectories keeps it stable over multi-hour runs in which models trained on short simulations diverge. The resulting emulator reproduces the evolution of thousands of species with high fidelity, remains accurate for unseen chemical compositions and runs orders of magnitude faster than the numerical solver. These results open a new direction for emulating complex atmospheric systems: neural operators that predict the full chemical state over long windows and choose their own training data. Training under changing meteorology is the next step towards bringing near-explicit chemistry into Earth-system models.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.33771 [cs.AI] |
| (or arXiv:2610.33771v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33771 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenqing Peng [view email]
[v1]
Sat, 24 Oct 2026 16:22:33 UTC (9,561 KB)
来源：arXiv cs.AI · arxiv.org