BUQEYE Guide to Projection-Based Emulators in Nuclear Physics

TL;DR

This paper introduces projection-based reduced-order emulators for nuclear physics, explaining their methods, benefits, and applications, with open-source tools for practitioners to adopt these fast, accurate models.

Contribution

It provides a pedagogical overview of projection-based emulators, detailing their implementation, advantages, and potential extensions in nuclear physics research.

Findings

Emulators enable fast, accurate calculations for nuclear models.

Projection methods like variational and Galerkin are effective.

Open-source Python tools facilitate adoption by practitioners.

Abstract

The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your EFT) presents a pedagogical introduction to projection-based, reduced-order emulators for applications in low-energy nuclear physics. The term emulator refers here to a fast surrogate model capable of reliably approximating high-fidelity models. As the general tools employed by these emulators are not yet well-known in the nuclear physics community, we discuss variational and Galerkin projection methods, emphasize the benefits of offline-online decompositions, and explore how these concepts lead to emulators for bound and scattering systems that enable fast & accurate calculations using many different model parameter sets. We also point to future extensions and applications of these emulators for nuclear physics, guided by the mature field of model (order) reduction. All examples discussed here and more are available as interactive, open-source Python code so that practitioners can readily adapt projection-based emulators for their own work.