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README.md

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 license: mit
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+This dataset is accompanying the NeurIPS'26 paper: Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events.
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+TLDR: We use a small ensemble of intentionally perturbed, low-cost simulations to identify the locally most unstable directions of the dynamics. That information is then used to guide learned corrections to coarse trajectories.
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+For a light-weight demonstration, see my blog post: [blog](https://www.mit.edu/~thieli/writing/neurips/).
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+Full details can be found in the accompanying paper: [arXiv:2609.30746](https://arxiv.org/abs/2609.30746).
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+The codebase can be found on the GitHub: [GitHub: thisisisi](https://github.com/thisisisi/ExtremeEventSensor)
+The code is organized into two subdirectories, `QG` and `LowDim`, which follow the same overall interface.