IMSRG-Net: A machine-learning-based solver for the in-medium similarity renormalization group method
Year: 2023
Type: article
Abstract: I present a 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 $\mathrm{\ensuremath{\Omega}}(s)$ 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 physics, i.e., the 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 $^{16}\mathrm{O}$ and $^{40}\mathrm{Ca}$. Furthermore, this model demonstrates effectiveness in deriving effective interactions for a valence space.
Source: Physical Review C
Author: Sota Yoshida
Institution: Utsunomiya University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4387399324