# An Efficient Learning Method to Connect Observables

arXiv: https://arxiv.org/abs/2503.01684 · HTML: https://mlsanity.com/paper/2503.01684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Galaxy Formation: Bayesian History Matching for the Observable Universe](https://mlsanity.com/paper/1405.4976/) (2014)

## Abstract (arXiv)

Constructing fast and accurate surrogate models is a key ingredient for making robust predictions in many topics. We introduce a new model, the Multiparameter Eigenvalue Problem (MEP) emulator. The new method connects emulators and can make predictions directly from observables to observables. We present that the MEP emulator can be trained with data from Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) emulators. A simple simulation on a one-dimensional lattice confirms the performance of the MEP emulator. Using 28O as an example, we also demonstrate that the predictive probability distribution of the target observables can be easily obtained through the new emulator.