7.8. Extra: Reduced Basis Method Emulators

Based on a talk by Dick Furnstahl. See the end for references.

Motivation for RBM emulators

Fig. 7.1 Screenshot from the end of a YouTube video called Timeline of the Discovery of Nuclides.

Fig. 7.2 Table of Nuclides showing progress in ab initio calculations (2024 version).

Simple model for eigenvalue RBM

Extensions

Fig. 7.3 Improved many-body perturbation theory (MBPT).

Fig. 7.4 Emulating pairing.

Fig. 7.5 Greedy algorithm for emulation.

Fig. 7.6 Cartoon summary of greedy algorithm.

References

References with nuclear applications

All of these have pointers to the broad RBM literature.

• Eigenvector Continuation and Projection-Based Emulators (Duguet et al., 2024)

• BUQEYE Guide to Projection-Based Emulators in Nuclear Physics (Drischler et al., 2022)

• Model reduction methods for nuclear emulators (Melendez et al., 2022)

• Training and Projecting: A reduced basis method emulator for many-body physics (Bonilla et al., 2022)

• Bayes goes fast: Uncertainty Quantification for a Covariant Energy Density Functional emulated by the Reduced Basis Method (Giuliani et al, 2023)

• ROSE: A reduced-order scattering emulator for optical models (Odell et al., 2024)

Jupyter and Quarto books for nuclear applications

• BUQEYE Guide to Projection-Based Emulators in Nuclear Physics

• Reduced Basis Methods in Nuclear Physics

Websites

• BAND (Bayesian Analysis of Nuclear Dynamics)

• BUQEYE (Bayesian Uncertainity Quantification: Errors for Your EFT)