Discovering Nuclear Models from Symbolic Machine Learning

• URL: http://arxiv.org/abs/2404.11477v3
• Date: Wed, 3 Jul 2024 14:47:09 GMT
• Title: Discovering Nuclear Models from Symbolic Machine Learning
• Authors: Jose M. Munoz, Silviu M. Udrescu, Ronald F. Garcia Ruiz,
• Abstract summary: We explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models. We developed a Multi-objective Iterated Regression approach that handles symbolic regressions over multiple target observables. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Related papers

• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Diffeomorphic Measure Matching with Kernels for Generative Modeling [1.2058600649065618]
  This article presents a framework for transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs) A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification.
  arXiv  Detail & Related papers  (2024-02-12T21:44:20Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• NuCLR: Nuclear Co-Learned Representations [0.0]
  We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables. We report an intriguing finding that the learned representation of NuCLR exhibits the prominent emergence of crucial aspects of the nuclear shell model. This suggests that the model is capable of capturing the underlying physical principles and that our approach has the potential to offer valuable insights into nuclear theory.
  arXiv  Detail & Related papers  (2023-06-09T17:59:16Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Solving the nuclear pairing model with neural network quantum states [58.720142291102135]
  We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism. A memory-efficient version of the reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian.
  arXiv  Detail & Related papers  (2022-11-09T00:18:01Z)
• Spectral density reconstruction with Chebyshev polynomials [77.34726150561087]
  We show how to perform controllable reconstructions of a finite energy resolution with rigorous error estimates. This paves the way for future applications in nuclear and condensed matter physics.
  arXiv  Detail & Related papers  (2021-10-05T15:16:13Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Quantified limits of the nuclear landscape [0.24792948967354234]
  Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory. We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies. The extrapolations obtained in this study will be put through stringent tests when new experimental information on exotic nuclei becomes available.
  arXiv  Detail & Related papers  (2020-01-16T16:25:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.