Machine learning in nuclear physics at low and intermediate energies
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Title
Machine learning in nuclear physics at low and intermediate energies
Authors
Keywords
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Journal
Science China-Physics Mechanics & Astronomy
Volume 66, Issue 8, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-07-05
DOI
10.1007/s11433-023-2116-0
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Note: Only part of the references are listed.- Verification of neutron-induced fission product yields evaluated by a tensor decompsition model in transport-burnup simulations
- (2023) Qu-Fei Song et al. Nuclear Science and Techniques
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- (2023) Shengli Chen et al. Physical Review Applied
- Magnetic control of tokamak plasmas through deep reinforcement learning
- (2022) Jonas Degrave et al. NATURE
- A new method to improve the generalization ability of neural networks: A case study of nuclear mass training
- (2022) Tianliang Zhao et al. NUCLEAR PHYSICS A
- Commissioning of a high-resolution collinear laser spectroscopy apparatus with a laser ablation ion source
- (2022) Shi-Wei Bai et al. Nuclear Science and Techniques
- Determination of neutron-skin thickness using configurational information entropy
- (2022) Chun-Wang Ma et al. Nuclear Science and Techniques
- Nuclear mass table in deformed relativistic Hartree–Bogoliubov theory in continuum, I: Even–even nuclei
- (2022) Kaiyuan Zhang et al. ATOMIC DATA AND NUCLEAR DATA TABLES
- Determination of impact parameter in high-energy heavy-ion collisions via deep learning
- (2022) Pei Xiang et al. Chinese Physics C
- Target dependence of isotopic cross sections in the spallation reactions $^{238}$U + p, d and $^{9}$Be at 1A GeV
- (2022) Qufei Song et al. Chinese Physics C
- Improved phenomenological nuclear charge radius formulae with kernel ridge regression
- (2022) Jian-Qin Ma et al. Chinese Physics C
- Bayesian evaluation of residual production cross sections in proton induced nuclear spallation reactions
- (2022) Dan Peng et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Iterative Bayesian Monte Carlo for nuclear data evaluation
- (2022) Erwin Alhassan et al. Nuclear Science and Techniques
- Research on tune feedback of the Hefei Light Source II based on machine learning
- (2022) Yong-Bo Yu et al. Nuclear Science and Techniques
- Nuclear mass based on the multi-task learning neural network method
- (2022) Xing-Chen Ming et al. Nuclear Science and Techniques
- Machine Learning Hidden Symmetries
- (2022) Ziming Liu et al. PHYSICAL REVIEW LETTERS
- Nuclear Mass Predictions of the Relativistic Density Functional Theory with the Kernel Ridge Regression and the Application to r-Process Simulations
- (2022) Lihan Guo et al. Symmetry-Basel
- Application of kernel ridge regression in predicting neutron-capture reaction cross-sections
- (2022) Tianxing Huang et al. COMMUNICATIONS IN THEORETICAL PHYSICS
- Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs
- (2022) Zi-Yuan Li et al. Nuclear Science and Techniques
- Study of nuclear low-lying excitation spectra with the Bayesian neural network approach
- (2022) Y.F. Wang et al. PHYSICS LETTERS B
- Neural network reconstruction of the dense matter equation of state from neutron star observables
- (2022) Shriya Soma et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Unified description of α decay and cluster radioactivity using the neural network approach and universal decay law
- (2022) TianLiang Zhao et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Ab initio predictions link the neutron skin of 208Pb to nuclear forces
- (2022) Baishan Hu et al. Nature Physics
- Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei
- (2022) Hui-Ling Wei et al. Nuclear Science and Techniques
- Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
- (2022) X.H. Wu et al. PHYSICS LETTERS B
- Colloquium : Machine learning in nuclear physics
- (2022) Amber Boehnlein et al. REVIEWS OF MODERN PHYSICS
- A consistent description of the relativistic effects and three-body interactions in atomic nuclei
- (2022) Y.L. Yang et al. PHYSICS LETTERS B
- Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning
- (2022) Yongjia Wang et al. PHYSICS LETTERS B
- The NUBASE2020 evaluation of nuclear physics properties
- (2021) Meng 王猛 Wang Chinese Physics C
- The Ame2020 atomic mass evaluation (II). Tables, graphs and references
- (2021) Meng 王猛 Wang Chinese Physics C
- Charge radii of exotic potassium isotopes challenge nuclear theory and the magic character of N = 32
- (2021) Á. Koszorús et al. Nature Physics
- Neutron-gamma discrimination method based on blind source separation and machine learning
- (2021) Hanan Arahmane et al. Nuclear Science and Techniques
- Determining the temperature in heavy-ion collisions with multiplicity distribution
- (2021) Yi-Dan Song et al. PHYSICS LETTERS B
- Bayesian inference on the isospin splitting of nucleon effective mass from giant resonances in Pb208
- (2021) Z. Zhang et al. Chinese Physics C
- A.I. for nuclear physics
- (2021) Paulo Bedaque et al. EUROPEAN PHYSICAL JOURNAL A
- Get on the BAND wagon: A Bayesian framework for quantifying model uncertainties in nuclear dynamics
- (2021) Daniel R Phillips et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Adaptability of n–$$\upgamma$$ discrimination and filtering methods based on plastic scintillation
- (2021) Zhuo Zuo et al. Nuclear Science and Techniques
- The description of giant dipole resonance key parameters with multitask neural networks
- (2021) J.H. Bai et al. PHYSICS LETTERS B
- Magnetic moment predictions of odd-$A$ nuclei with the Bayesian neural network approach
- (2021) Zilong Yuan et al. Chinese Physics C
- Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System
- (2021) Hong-Bin Ren et al. CHINESE PHYSICS LETTERS
- Ability of the radial basis function approach to extrapolate nuclear mass
- (2021) Tao Li et al. COMMUNICATIONS IN THEORETICAL PHYSICS
- Highly accurate protein structure prediction for the human proteome
- (2021) Kathryn Tunyasuvunakool et al. NATURE
- Vertex and energy reconstruction in JUNO with machine learning methods
- (2021) Zhen Qian et al. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
- Yield of long-lived fission product transmutation using proton-, deuteron-, and alpha particle-induced spallation
- (2021) Meng-Ting Jin et al. Nuclear Science and Techniques
- $$\alpha$$-clustering effect on flows of direct photons in heavy-ion collisions
- (2021) Chen-Zhong Shi et al. Nuclear Science and Techniques
- Variational Monte Carlo Calculations of A≤4 Nuclei with an Artificial Neural-Network Correlator Ansatz
- (2021) Corey Adams et al. PHYSICAL REVIEW LETTERS
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- (2021) Zu-Xing Yang et al. PHYSICS LETTERS B
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- (2021) X.H. Wu et al. PHYSICS LETTERS B
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- (2021) Bao-An Li et al. Universe
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- (2021) Xiao-Zhe Li et al. Nuclear Science and Techniques
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- (2021) Jie Liu et al. Nuclear Science and Techniques
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- (2021) Ze-Peng Gao et al. Nuclear Science and Techniques
- Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
- (2021) Yongjia Wang et al. PHYSICS LETTERS B
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- (2020) Raphaël-David Lasseri et al. PHYSICAL REVIEW LETTERS
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- (2020) L. Yang et al. PHYSICS LETTERS B
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- (2020) Fupeng Li et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
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- (2020) J.W.T. Keeble et al. PHYSICS LETTERS B
- Diffuseness effect and radial basis function network for optimizing α decay calculations *
- (2020) Na-Na Ma et al. Chinese Physics C
- A high-bias, low-variance introduction to Machine Learning for physicists
- (2019) Pankaj Mehta et al. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
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- (2019) Léo Neufcourt et al. PHYSICAL REVIEW LETTERS
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- (2019) Vojtěch Havlíček et al. NATURE
- Mass predictions of the relativistic continuum Hartree-Bogoliubov model with radial basis function approach
- (2019) Min Shi et al. Chinese Physics C
- Bayesian optimization in ab initio nuclear physics
- (2019) A Ekström et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Bayesian Evaluation of Incomplete Fission Yields
- (2019) Zi-Ao Wang et al. PHYSICAL REVIEW LETTERS
- Machine learning methods for track classification in the AT-TPC
- (2019) M.P. Kuchera et al. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
- Solving many-electron Schrödinger equation using deep neural networks
- (2019) Jiequn Han et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Constraining the symmetry energy with heavy-ion collisions and Bayesian analyses
- (2019) P. Morfouace et al. PHYSICS LETTERS B
- Machine learning and the physical sciences
- (2019) Giuseppe Carleo et al. REVIEWS OF MODERN PHYSICS
- Nuclear properties for astrophysical and radioactive-ion-beam applications (II)
- (2018) P. Möller et al. ATOMIC DATA AND NUCLEAR DATA TABLES
- Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice
- (2018) Hiroki Saito et al. JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
- Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
- (2018) Z.M. Niu et al. PHYSICS LETTERS B
- Nucleon effective masses in neutron-rich matter
- (2018) Bao-An Li et al. PROGRESS IN PARTICLE AND NUCLEAR PHYSICS
- High precision nuclear mass predictions towards a hundred kilo-electron-volt accuracy
- (2018) Zhongming Niu et al. Science Bulletin
- Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation
- (2018) Hao Qiao et al. Science China-Physics Mechanics & Astronomy
- Liquid–Gas phase transition in nuclei
- (2018) B. Borderie et al. PROGRESS IN PARTICLE AND NUCLEAR PHYSICS
- Performance of the Levenberg–Marquardt neural network approach in nuclear mass prediction
- (2017) Hai Fei Zhang et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Nuclear ground-state masses and deformations: FRDM(2012)
- (2016) P. Möller et al. ATOMIC DATA AND NUCLEAR DATA TABLES
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Weizsäcker–Skyrme-type mass formula by considering radial basis function correction
- (2015) Na Na Ma et al. JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Mass predictions of the relativistic mean-field model with the radial basis function approach
- (2014) J. S. Zheng et al. PHYSICAL REVIEW C
- Superheavy magic structures in the relativistic Hartree–Fock–Bogoliubov approach
- (2014) Jia Jie Li et al. PHYSICS LETTERS B
- Radial basis function approach in nuclear mass predictions
- (2013) Z. M. Niu et al. PHYSICAL REVIEW C
- Table of experimental nuclear ground state charge radii: An update
- (2012) I. Angeli et al. ATOMIC DATA AND NUCLEAR DATA TABLES
- Modern Nuclear Data Evaluation with the TALYS Code System
- (2012) A.J. Koning et al. NUCLEAR DATA SHEETS
- Nuclear mass predictions with a radial basis function approach
- (2011) Ning Wang et al. PHYSICAL REVIEW C
- Decodingβ-decay systematics: A global statistical model forβ−half-lives
- (2009) N. J. Costiris et al. PHYSICAL REVIEW C
- Nuclear Symmetry Energy Probed by Neutron Skin Thickness of Nuclei
- (2009) M. Centelles et al. PHYSICAL REVIEW LETTERS
- Constraints on the Density Dependence of the Symmetry Energy
- (2009) M. B. Tsang et al. PHYSICAL REVIEW LETTERS
- Recent progress and new challenges in isospin physics with heavy-ion reactions
- (2008) B LI et al. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
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