机制感知的集成条件化用于数据有限极端事件仿真
Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events
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中文总结 AI 辅助
提出机制感知集成条件化框架，利用集成协方差作为局部不稳定性代理，通过FiLM模块注入条件化信号，在低维混沌和准地转流中显著提升数据有限下极端事件仿真精度。
中文摘要 AI 辅助
混沌系统中的极端事件难以从短轨迹中学习，因为它们受瞬态有限时间不稳定性控制，而非频繁观测的体态动力学。我们提出一种机制感知的条件化即插即用框架，将微调粗粒度集成转化为局部不稳定性几何的非侵入式传感器。在小噪声情况下，集成协方差聚合了控制局部不稳定性的相同有限时间变形核，为同步粗轨迹周围的局部放大结构提供了免雅可比代理。一个小型FiLM模块将该集成几何统计注入到原本不变的骨干网络中，同时保持粗粒度模拟器不变。我们在两条不同的流程中展示了该接口：一个用于受控低维混沌系统的Transformer风格残差注意力修正器，以及一个用于地形两层准地转（QG）流的概率循环STORN修正器。在低维基准中，集成协方差方向与OTD模态共同激活，FiLM条件化将99百分位超阈值频率误差优于无上下文Transformer基线。在QG中，仅在50个时间单位上训练的固定集成条件化FiLM-STORN模型，在数据有限机制下显著改善了长时程罕见事件统计，包括密度尾部误差、超阈值频率和空间超阈值面积分布，相对于在相同数据上训练的无条件STORN；在平均高阈值超阈值诊断上，它也优于使用20倍高分辨率数据训练的基线STORN。这些结果表明，局部不稳定性几何不仅是事后可解释的，而且是数据高效罕见事件仿真的可操作条件化信号。
英文摘要
Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed bulk dynamics. We propose a mechanism-aware conditioning plug-in framework that turns a nudged coarse ensemble into a non-intrusive sensor of local instability geometry. In the small-noise regime, the ensemble covariance aggregates the same finite-time deformation kernels that govern local instability, providing a Jacobian-free proxy for the local amplification structure around a synchronized coarse trajectory. A small FiLM module injects statistics of this ensemble geometry into an otherwise unchanged backbone while leaving the coarse simulator unchanged. We demonstrate this interface in two distinct pipelines: a Transformer-style residual-attention corrector for a controlled low-dimensional chaotic system and a probabilistic recurrent STORN corrector for topographic two-layer quasi-geostrophic (QG) flow. In the low-dimensional benchmark, ensemble covariance directions co-activate with OTD modes and FiLM conditioning improves 99th-percentile exceedance-frequency errors over an identical no-context Transformer baseline. In QG, a fixed ensemble-conditioned FiLM-STORN model trained on only \(50\) time units substantially improves long-horizon rare-event statistics in the data-limited regime, including density-tail errors, exceedance frequencies, and spatial exceedance-area distributions relative to an unconditioned STORN trained on the same data; on averaged high-threshold exceedance diagnostics, it also outperforms the baseline STORN trained with $20$ times more high-resolution data. These results show that local instability geometry is not merely interpretable post hoc, but an actionable conditioning signal for data-efficient rare-event emulation.
发表机构
- Massachusetts Institute of Technology（麻省理工学院）
- Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
