IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group

Abstract

We present a novel method, IMSRG-Net, which utilizes machine learning techniques as a solver for the in-medium Similarity Renormalization Group (IMSRG). The primary objective of IMSRG-Net is to approximate the Magnus operators $\Omega (s)$ in the IMSRG flow equation, thereby offering an alternative to the computationally intensive part of IMSRG calculations. The key idea of IMSRG-Net is its design of the loss function inspired by physics-informed neural networks to encode the underlying {\it physics}, i.e., IMSRG flow equation, into the model. Through training on a dataset comprising ten data points with flow parameters up to $s=20$, capturing approximately one-eighth to one-quarter of the entire flow, IMSRG-Net exhibits remarkable accuracy in extrapolating the ground state energies and charge radii of ${}^{16}$O and ${}^{40}$Ca. Furthermore, this model demonstrates effectiveness in deriving effective interactions for a valence space.

8 pages, accepted version for Phys. Rev. C

References in corpus (8)

• In-Medium Similarity Renormalization Group for Nuclei
• Peripheral nucleon-nucleon scattering at fifth order of chiral perturbation theory
• In-Medium Similarity Renormalization Group for Open-Shell Nuclei
• In-medium similarity renormalization group with three-body operators
• Model reduction methods for nuclear emulators
• Fast & accurate emulation of two-body scattering observables without wave functions
• Generalizing the calculable $R$-matrix theory and eigenvector continuation to the incoming wave boundary condition
• Eigenvector continuation for emulating and extrapolating two-body resonances

Cited by in corpus (1)

• Global Framework for Emulation of Nuclear Calculations