NeurIPS 2026 · 2026

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

NeurIPS'26 oral

Isabella S. Thiel1Juan Bello-Rivas2Yannis G. Kevrekidis3Themistoklis P. Sapsis1
  1. 1 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA
  2. 2 Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD
  3. 3 Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
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Abstract

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.

BibTeX

@misc{thiel2026mechanismaware,
  title        = {Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events},
  author       = {Isabella S. Thiel and Juan Bello-Rivas and Yannis G. Kevrekidis and Themistoklis P. Sapsis},
  year         = {2026},
  eprint       = {2609.30746},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  doi          = {10.48550/arXiv.2609.30746},
  url          = {https://arxiv.org/abs/2609.30746}
}