Datasets:

thisisisi
/

ExtremeEventSensor

---
license: mit
tags:
- extreme
- instabilities
---
# ExtremeEventSensor

This dataset accompanies the NeurIPS 2026 paper *Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events*.

**TLDR:** Small ensembles of perturbed, low-cost simulations reveal locally unstable directions in the dynamics. We use that information to guide learned corrections to coarse trajectories.

## What’s included

- **LowDim:** Trajectories from a three-dimensional nonlinear system exhibiting bursts, including reference, coarse, nudged, and perturbed ensemble simulations.
- **QG:** Data from a two-layer quasigeostrophic model, including upper and lower streamfunction with additional topography.  Includes fields from reference, coarse, nudged, and ensemble simulations.


For a short demonstration, see the [blog post](https://www.mit.edu/~thieli/writing/neurips/). Full details about methods and examples are in the [paper](https://arxiv.org/abs/2609.30746), and the accompanying code is on [GitHub](https://github.com/thisisisi/ExtremeEventSensor).

	---
	license: mit
	tags:
	- extreme
	- instabilities
	---

	# ExtremeEventSensor

	This dataset accompanies the NeurIPS 2026 paper Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events.

	TLDR: Small ensembles of perturbed, low-cost simulations reveal locally unstable directions in the dynamics. We use that information to guide learned corrections to coarse trajectories.

	## What’s included

	- LowDim: Trajectories from a three-dimensional nonlinear system exhibiting bursts, including reference, coarse, nudged, and perturbed ensemble simulations.
	- QG: Data from a two-layer quasigeostrophic model, including upper and lower streamfunction with additional topography. Includes fields from reference, coarse, nudged, and ensemble simulations.


	For a short demonstration, see the [blog post](https://www.mit.edu/~thieli/writing/neurips/). Full details about methods and examples are in the [paper](https://arxiv.org/abs/2609.30746), and the accompanying code is on [GitHub](https://github.com/thisisisi/ExtremeEventSensor).