Ml For Fusion

Uncertainty Quantification

Knowing how much to trust a model's prediction, and communicating that alongside the number.

Why it is central in fusion

A prediction without uncertainty is a hazard in a domain where actions are costly and regimes are new. Uncertainty quantification (UQ) attaches a measure of confidence to each prediction, so downstream decisions can be cautious where the model is unsure and confident where it is well supported.

Two kinds of uncertainty

Kronos motion — fusion

Aleatoric: irreducible noise in the data itself
Epistemic: the model's ignorance, reducible with more data

Epistemic uncertainty is what grows when a model extrapolates outside its training data, which is exactly the fusion situation of predicting new regimes. Capturing it is essential; a model that reports only noise-level uncertainty will look confident precisely where it should not be.

Methods

Gaussian processes give epistemic uncertainty directly. For neural networks, deep ensembles (training several models and comparing them), Monte Carlo dropout, and Bayesian neural networks approximate it. Conformal prediction gives distribution-free coverage guarantees. Each has trade-offs in cost and calibration.

Calibration

An uncertainty estimate is only useful if it is calibrated: when a model says it is 90 percent confident, it should be right about 90 percent of the time. Calibration must be checked on held-out data, and recalibrated if the model is overconfident, which neural networks often are.

Using UQ

In control, high uncertainty triggers caution or a fallback. In design, uncertainty is propagated through to the quantities that matter, so margins reflect real confidence. In evaluation, wide uncertainty on a rare-event metric prevents over-claiming from few examples. UQ turns a point prediction into an honest one.