Machine learning light hypernuclei

Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron

(2021) Esra Yüksel et al.   INTERNATIONAL JOURNAL OF MODERN PHYSICS E

A.I. for nuclear physics

(2021) Paulo Bedaque et al.   EUROPEAN PHYSICAL JOURNAL A

Systematic Nuclear Uncertainties in the Hypertriton System

(2021) Thiri Yadanar Htun et al.   FEW-BODY SYSTEMS

Unveiling the nuclear matter EoS from neutron star properties: a supervised machine learning approach

(2021) Márcio Ferreira et al.   JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS

Nuclear masses in extended kernel ridge regression with odd-even effects

(2021) X.H. Wu et al.   PHYSICS LETTERS B

Machine learning the nuclear mass

(2021) Ze-Peng Gao et al.   Nuclear Science and Techniques

Trees and Forests in Nuclear Physics

(2020) Marco Carnini et al.   JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS

Taming Nuclear Complexity with a Committee of Multilayer Neural Networks

(2020) Raphaël-David Lasseri et al.   PHYSICAL REVIEW LETTERS

Machine learning the deuteron

(2020) J.W.T. Keeble et al.   PHYSICS LETTERS B

A high-bias, low-variance introduction to Machine Learning for physicists

(2019) Pankaj Mehta et al.   PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

Neutron Drip Line in the Ca Region from Bayesian Model Averaging

(2019) Léo Neufcourt et al.   PHYSICAL REVIEW LETTERS

Alpha half-lives calculation of superheavy nuclei with Qα-values predictions based on Bayesian neural network approach

(2019) Ubaldo Baños Rodríguez et al.   JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS

Estimation of fusion reaction cross-sections by artificial neural networks

(2019) Serkan Akkoyun   NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

(2018) Z.M. Niu et al.   PHYSICS LETTERS B

Nuclear charge radii: density functional theory meets Bayesian neural networks

(2016) R Utama et al.   JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS

Ground-state binding energy of H Λ 4 from high-resolution decay-pion spectroscopy

(2016) F. Schulz et al.   NUCLEAR PHYSICS A

Strangeness in nuclear physics

(2016) A. Gal et al.   REVIEWS OF MODERN PHYSICS

Deep learning

(2015) Yann LeCun et al.   NATURE

Zemach moments of the proton from Bayesian inference

(2015) Krzysztof M. Graczyk et al.   PHYSICAL REVIEW C

Proton radius from Bayesian inference

(2014) Krzysztof M. Graczyk et al.   PHYSICAL REVIEW C

Ab InitioDescription ofp-Shell Hypernuclei

(2014) Roland Wirth et al.   PHYSICAL REVIEW LETTERS

An artificial neural network application on nuclear charge radii

(2013) S Akkoyun et al.   JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS

Ab initio no core shell model

(2012) Bruce R. Barrett et al.   PROGRESS IN PARTICLE AND NUCLEAR PHYSICS

Decodingβ-decay systematics: A global statistical model forβ−half-lives

(2009) N. J. Costiris et al.   PHYSICAL REVIEW C