:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Brady, Christopher, Wu, Xu
Format:	Preprint
Published:	2025
Subjects:	Nuclear Theory Machine Learning
Online Access:	https://arxiv.org/abs/2509.07790
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Global Framework for Emulation of Nuclear Calculations
by: Belley, Antoine, et al.
Published: (2025)

Discovering Nuclear Models from Symbolic Machine Learning
by: Munoz, Jose M., et al.
Published: (2024)

Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics
by: Gorard, Jonathan, et al.
Published: (2025)

Physics Informed Bayesian Machine Learning of Sparse and Imperfect Nuclear Data
by: Liu, Jiaming, et al.
Published: (2026)

Muon Nuclear Data Development Project
by: Watanabe, Yukinobu, et al.
Published: (2026)

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data
by: Fukushima, Kenji, et al.
Published: (2025)

ENDF/B-VIII.1: Updated Nuclear Reaction Data Library for Science and Applications
by: Nobre, G. P. A., et al.
Published: (2025)

Bayesian Inference of Nuclear-Matter Density from Proton Scattering
by: Zamora, J. C.
Published: (2025)

Multi-Cycle PWR Modeling with DRAGON and PARCS: Implications for Nuclear Data Assessment
by: Meunier, Benjamin Arthur Hugo
Published: (2025)

The DNA of nuclear models: How AI predicts nuclear masses
by: Richardson, Kate A., et al.
Published: (2025)

From Neurons to Neutrons: A Case Study in Interpretability
by: Kitouni, Ouail, et al.
Published: (2024)

Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model
by: Bentley, I., et al.
Published: (2025)

Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data
by: Imbrišak, M., et al.
Published: (2025)

Sim-to-real supervised domain adaptation for radioisotope identification
by: Lalor, Peter, et al.
Published: (2024)

Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis
by: Giuliani, Pablo, et al.
Published: (2024)

Constraints on the Equation of State of Nuclear Matter from Systematically Comparing SMASH Calculations to HADES Data
by: Mohs, Justin, et al.
Published: (2024)

Small Ensemble-based Data Assimilation: A Machine Learning-Enhanced Data Assimilation Method with Limited Ensemble Size
by: Li, Zhilin, et al.
Published: (2025)

Principal Components of Nuclear Mass Model Residuals
by: Huang, Y. Y., et al.
Published: (2025)

Automated Resonance Identification in Nuclear Data Evaluation
by: Walton, Noah A. W., et al.
Published: (2024)

High-fidelity Nuclear Coherent Population Transfer via the Mixed-State Inverse Engineering
by: Wang, Ying, et al.
Published: (2024)

Data-driven Approach for Interpolation of Sparse Data
by: Ferguson, R. F., et al.
Published: (2025)

Basis Representation for Nuclear Densities from Principal Component Analysis
by: Lv, Chen-Jun, et al.
Published: (2026)

Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning
by: Lu, Yanhua, et al.
Published: (2026)

Light Nuclei embedded in a Nuclear Medium: Clustering and Mott Transitions
by: Meng, Qi, et al.
Published: (2026)

A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functionals
by: Schunck, N., et al.
Published: (2020)

High-Dimensional Bayesian Calibration of Expensive Nuclear Models with Differentiable Emulation
by: Lei, Jin
Published: (2026)

Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
by: Kowal, Beata E., et al.
Published: (2025)

Nuclear transitions on demand
by: Yang, C. -J.
Published: (2025)

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
by: Aarts, Gert, et al.
Published: (2025)

Predictions of charge density distributions for nuclei with $Z \geq 8$
by: Wang, Yun Dong, et al.
Published: (2026)

An Efficient Learning Method to Connect Observables
by: Yu, Hang, et al.
Published: (2025)

Extrapolation to infinite model space of no-core shell model calculations using machine learning
by: Mazur, Aleksandr, et al.
Published: (2025)

Nuclear Isomers at the Extremes of their Properties
by: Maheshwari, Bhoomika, et al.
Published: (2024)

Nucleonic Shells and Nuclear Masses
by: Buskirk, Landon, et al.
Published: (2023)

Nuclear PDFs After the First Decade of LHC Data
by: Klasen, M., et al.
Published: (2023)

Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)
by: Yiu, To Chung, et al.
Published: (2023)

Nuclear Rainbow of Core-Symmetric Systems
by: Phuc, Nguyen Tri Toan, et al.
Published: (2026)

Nuclear charge radii of silicon isotopes
by: König, Kristian, et al.
Published: (2023)

Nuclear Beavers
by: Wylie, Joshua, et al.
Published: (2025)

Illuminating Systematic Trends in Nuclear Data with Generative Machine Learning Models
by: Fox, Jordan M. R., et al.
Published: (2024)