NANO方法解决混合模型蒸散发分解的多解性问题
Non-Interfering Asymmetric AdamW Updates for Constrained Exploration of Equifinality in Hybrid Models
这篇论文讲了个挺实际的问题：模型拟合同一批数据，内部机制却可以有完全不同的解。NANO 方法让训练主动制造多样性又不破坏拟合，做混合建模的可以看看。
arXiv 论文研究了混合模型中的等效性（equifinality）问题：观测数据只能约束总蒸散发通量，无法约束其内部蒸腾与蒸发的分配比例。仅靠随机种子构建的模型集成只覆盖数据允许的分配范围中很窄的一带。论文提出 NANO（Non-interfering Asymmetric Network Optimization），在 AdamW 下的辅助梯度与主梯度原始正交不再保证互不干扰，NANO 使更新对主损失的一阶影响与仅用主目标时完全一致，辅助步长有界，并将排斥集中在主梯度闲置的坐标上。
Non-Interfering Asymmetric AdamW Updates for Constrained Exploration of Equifinality in Hybrid Models
Hybrid models let neural networks supply the unresolved parameters of a process-based simulator, and are fitted to observations that constrain aggregate fluxes and storages but not the internal partitioning that produces them. Such a fit is equifinal: configurations indistinguishable in the observed aggregates can disagree substantially about the unobserved internals, and therefore about the mechanism they attribute to the same data. We study evapotranspiration, whose total flux is observed while its split into transpiration and evaporation is not. A model ensemble built from random seeds alone spans only a narrow band of the partitionings the data admit. We produce the spread deliberately, training members under auxiliary repulsion on transpiration, subject to the fit to observations not degrading. Non-interference is usually arranged by making the auxiliary gradient orthogonal to the main one, but under AdamW the applied step is a preconditioned momentum direction, so orthogonality between raw gradients no longer implies it. We introduce Non-interfering Asymmetric Network Optimization (NANO), an update whose first-order effect on the main loss is exactly that of the main objective alone, whose auxiliary step is bounded, and which concentrates repulsion in the coordinates the main gradient leaves idle.