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

Sota Yoshida

AI summary · arXiv: 2306.08878v1 · nucl-th

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

Sota Yoshida

Original paper Submitted to arXiv on 15 Jun. 2023 7 pages including supplemental material License: CC BY 4.0

How to read this summary? Illustrated guide

The essentials

The authors present a novel method called IMSRG-Net which utilizes machine learning techniques as a solver for the in-medium Similarity Renormalization Group (IMSRG)
IMSRG-Net approximates the Magnus operators $\Omega(s)$ in the IMSRG flow equation, reducing computational costs
The loss function of IMSRG-Net is inspired by physics-informed neural networks, allowing it to encode underlying physics into its structure
By training on a dataset consisting of ten data points with flow parameters up to $s = 20$, IMSRG-Net demonstrates remarkable accuracy in extrapolating ground state energies and charge radii of ${}^{16}$O and ${}^{40}$Ca
IMSRG-Net is effective in deriving effective interactions for a valence space
The work was supported by JSPS KAKENHI (Grants No. 22K14030)
References [1] to [15] are cited throughout the paper providing additional context and background information on related studies
This work presents a promising approach combining machine learning techniques with physics-based knowledge to solve complex problems in nuclear physics

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: IMSRG-Net Machine Learning Magnus Operators Physics-Informed Neural Networks IMSRG Flow Equation

Read further

Comprehensive Summary
Layman's Summary
Blog article
Original abstract

In this study, the authors present a novel method called 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, offering an alternative to computationally intensive parts of IMSRG calculations. The key innovation of IMSRG-Net lies in its design of the loss function which is inspired by physics-informed neural networks. This allows the model to encode underlying physics, specifically the IMSRG flow equation into its structure. By training on a dataset consisting of ten data points with flow parameters up to $s = 20$, capturing approximately one-eighth to one-quarter of the entire flow, IMSRG-Net demonstrates remarkable accuracy in extrapolating ground state energies and charge radii of ${}^{16}$O and ${}^{40}$Ca. Furthermore, this model proves effective in deriving effective interactions for a valence space. The authors acknowledge that their work was supported by JSPS KAKENHI (Grants No. 22K14030). This study builds upon previous research in this field and references [1] to [15] are cited throughout the paper providing additional context and background information on related studies. Overall, this work presents a promising approach combining machine learning techniques with physics based knowledge to solve complex problems in nuclear physics. The use of IMSRG-Net as a solver for IMSRG calculations has shown great potential for accurately predicting ground state energies and charge radii while reducing computational costs.

Introducing IMSRG-Net: A Novel Machine Learning Approach to Solving In-Medium Similarity Renormalization Group (IMSRG) Calculations

In a recent study, researchers from the University of Tokyo and RIKEN Nishina Center have developed a novel method called IMSRG-Net which utilizes machine learning techniques as a solver for in-medium Similarity Renormalization Group (IMSRG) calculations. This approach offers an alternative to computationally intensive parts of IMSRG calculations while still accurately predicting ground state energies and charge radii. The authors acknowledge that their work was supported by JSPS KAKENHI (Grants No. 22K14030).

Background Information on In-Medium Similarity Renormalization Group Calculations

In order to understand this research paper, it is important to first provide some background information on in-medium similarity renormalization group (IMSRG) calculations. These calculations are used in nuclear physics to calculate properties such as ground state energies and charge radii of nuclei. They are based on the Magnus expansion, which is an approximation of the time evolution operator $\hat{U}(s)$ with respect to flow parameter $s$. This expansion can be written as $\hat{U}(s)=\exp[\Omega(s)]$, where $\Omega(s)$ is known as the Magnus operator. It can be expressed using a series of nested commutators, each containing two Hamiltonians at different values of $s$. This makes calculating $\Omega(s)$ very computationally expensive since it requires multiple iterations over all possible combinations of Hamiltonians at different values of $s$.

The Key Innovation Behind IMSRG-Net

The key innovation behind IMSRG-Net lies in its design of the loss function which is inspired by physics informed neural networks. This allows the model to encode underlying physics, specifically the IMSRG flow equation into its structure. By training on a dataset consisting of ten data points with flow parameters up to $s = 20$, capturing approximately one eighth to one quarter of the entire flow, IMSRG Net demonstrates remarkable accuracy in extrapolating ground state energies and charge radii for ${}^{16}$O and ${}^{40}$Ca nuclei. Furthermore, this model proves effective in deriving effective interactions for valence spaces too.

Conclusion

Overall, this work presents a promising approach combining machine learning techniques with physics based knowledge to solve complex problems in nuclear physics such as calculating ground state energies and charge radii while reducing computational costs significantly compared with traditional methods like Monte Carlo simulations or exact diagonalizations. The use of IMSRG Net has shown great potential for accurately predicting these values without sacrificing accuracy or speed when compared with other approaches currently available today.

Created on 20 Sep. 2023

Was this synthesis useful?

Score: 0

Ask questions about this paper to our AI assistant

How to chat with this paper? Illustrated guide

Compare with the PDF

You can also chat with multiple papers at once here.

AI assistant instructions?

Tools for this paper

Explain it differently 3 free rewrites left today

Save to a collection Group papers in collections to compare them, chat with them and export them. Create a free account Log in

Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Markdown (Notion, Obsidian)

Advanced tools Premium

For researchers who follow a field closely: citation alerts, journal-club slides, version comparison and Zotero export. Discover Premium

Zotero

Citation alerts Citation alerts guide

Who works on it? The people and institutions who publish on its topic

Journal club Premium+ Slides to present it

Versions What changed between versions

Talk about it in public Premium+ Press kit: release, questions, hype check Press kit guide

Papers around this one

See them on a map

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

Related
Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
Valéria Carvalho, Márcio Ferreira, Tuhin Malik et al. · 2023 · Nuclear Theory · Not summarized yet

Abstract
We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS radius and tidal deformability, we leverage BNNs as effective tools for inferring the proton fraction and sound speed within NS interiors. To achieve this, several BNNs models were developed upon a dataset of $\sim$ 25K nuclear EoS within a relativistic mean-field framework, obtained through Bayesian inference that adheres to minimal low-density constraints. Unlike conventional neural networks, BNNs possess an exceptional quality: they provide a prediction u…
Related
Investigating Dark Matter-Admixed Neutron Stars with NITR Equation of State in Light of PSR J0952-0607
Pinku Routaray, Sailesh Ranjan Mohanty, H. C. Das et al. · 2023 · Nuclear Theory · AI summary available

Abstract
The heaviest pulsar, PSR J0952-0607, with a mass of $M=2.35\pm0.17 \ M_\odot$, has recently been discovered in the disk of the Milky Way Galaxy. In response to this discovery, a new RMF model, "NITR" has been developed. The NITR model's naturalness has been confirmed by assessing its validity for various finite nuclei and nuclear matter (NM) properties, including incompressibility, symmetry energy, and slope parameter values of 225.11, 31.69, and 43.86 MeV, respectively. These values satisfy the empirical/experimental limits currently available. The maximum mass and canonical radius of a neutron star (NS) calculated using the NITR model parameters are 2.35 $M_\odot$ and 12.73 km, respectivel…
Related
Modeling the diffusive dynamics of critical fluctuations near the QCD critical point
Marlene Nahrgang, Marcus Bluhm · 2020 · Phys. Rev. D 102, 094017 (2020) · AI summary available

Abstract
The experimental search for the QCD critical point by means of relativistic heavy-ion collisions necessitates the development of dynamical models of fluctuations. In this work we study the fluctuations of the net-baryon density near the critical point. Due to net-baryon number conservation the correct dynamics is given by the fluid dynamical diffusion equation, which we extend by a white noise stochastic term to include intrinsic fluctuations. We quantify finite resolution and finite size effects by comparing our numerical results to analytic expectations for the structure factor and the equal-time correlation function. In small systems the net-baryon number conservation turns out to be quan…
Related
Electromagnetic isoscalar rho-pi-gamma exchange current and the anomalous action
E. Truhlik, J. Smejkal, F. C. Khanna · 2000 · Nucl.Phys. A689 (2001) 741-752 · Not summarized yet

Abstract
Using modern data, we first refit constants needed to complete an anomalous pi-rho-omega-a_1 Lagrangian obtained within the approach of hidden local symmetries. Then we derive from this Lagrangian electromagnetic isoscalar rho-pi-gamma and rho-a_1-gamma currents needed in calculations of the deuteron electromagnetic form factors at large momentum transfers.
Related
Probing Nuclear Structure of Heavy Ions at the Large Hadron Collider
Heikki Mäntysaari, Björn Schenke, Chun Shen et al. · 2024 · Nuclear Theory · AI summary available

Abstract
We perform high-statistics simulations to study the impacts of nuclear structure on the ratios of anisotropic flow observables in $^{208}$Pb+$^{208}$Pb and $^{129}$Xe+$^{129}$Xe collisions at the Large Hadron Collider. Even with $40\%$ difference in atomic numbers between $^{208}$Pb and $^{129}$Xe nuclei, the ratios of anisotropic flow in the same centrality class between the two collision systems are strongly affected by the nuclear structure inputs in the initial state. The ratios of $v_2\{4\}/v_2\{2\}$ in these collisions are sensitive to the nuclear skin thickness of the colliding nuclei, providing indirect constraints on the nuclei's neutron skin. Our model predictions serve as a benchm…
Related
Quantum computing of the $^6$Li nucleus via ordered unitary coupled clusters
Oriel Kiss, Michele Grossi, Pavel Lougovski et al. · 2022 · Phys. Rev. C 106, 034325 (2022) · AI summary available

Abstract
The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of quantum many-body systems. A key component of the algorithm and an active research area is the construction of a parametrized trial wavefunction -- a so called variational ansatz. The wavefunction parametrization should be expressive enough, i.e. represent the true eigenstate of a quantum system for some choice of parameter values. On the other hand, it should be trainable, i.e. the number of parameters should not grow exponentially with the size of the system. Here, we apply VQE to the problem of finding ground and excited state energies of the odd-odd nucleus $^6$Li. We study the effects…
Related
Multicomponent relativistic dissipative fluid dynamics from the Boltzmann equation
Jan A. Fotakis, Etele Molnár, Harri Niemi et al. · 2022 · Nuclear Theory · AI summary available

Abstract
We derive multicomponent relativistic second-order dissipative fluid dynamics from the Boltzmann equations for a reactive mixture of $N_{\text{spec}}$ particle species with $N_q$ intrinsic quantum numbers (e.g. electric charge, baryon number, and strangeness) using the method of moments. We obtain the continuity equations for multiple conserved charges as well as the conservation equations for the total energy and momentum in the single-fluid approximation. These $4+N_q$ conservation laws are closed by deriving the second-order equations of motion for the dissipative quantities in the $(10+4N_q)$-moment approximation. The resulting fluid-dynamical equations are formally similar to those of a…
Related
The Silver Blaze Problem in QCD
Thomas D. Cohen · 2026 · Nuclear Theory · AI summary available

Abstract
This article provides a pedagogical introduction to the Silver Blaze problem. This problem refers to the difficulty of reconciling to perspectives on QCD with a chemical potential. The first is the phenomenological fact that at $T=0$ QCD remains in its ground state -- the vacuum -- with all physical observables unchanged whenever the magnitude of a chemical potential is less than some critical value. The second is the fact that in functional integral treatments, the inclusion of any nonzero chemical potential changes all eigenvalues of the Dirac operator for every gauge configuration, leading to a natural expectation that the functional determinants also changes, which leads to the expectati…

Go further

How are these papers found?

We compare the title and abstract of this paper with those of every paper summarized on this site, and with the papers Semantic Scholar recommends, those it cites and those citing it (for a much-cited paper, its most recent citations and, from OpenAlex, the most cited ones). Among the prior work and the follow-ups, the papers that use each other in depth, a method it uses and the much-cited follow-ups come a little higher. The levels (very close, close, related) come from the similarity of the texts, not from a judgment of quality; * marks a paper without an abstract, compared by its title. The sentence under a paper is written by an AI from the two abstracts: check it in the papers. A paper marked not relevant by three readers leaves the list.

For now only among the papers summarized on this site: search again to add the prior work, the follow-ups and Semantic Scholar’s recommendations.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.