001 Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks arXiv Paper Phong Dang, Evander Espinoza et al. Jun 26 002 Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning arXiv Paper Jing-Zong Zhang, W. Zha et al. Jun 22 003 Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing arXiv Paper Jin Lei Jun 8 004 Product units in gated recurrent units improve nuclear-mass prediction arXiv Paper Ziyuan Li, P. S. Freitas et al. Jun 5 005 Neural Operators as Efficient Function Interpolators arXiv Paper V. Niarchos, Angelos Sirbu et al. May 8 006 Predictions of charge density distributions for nuclei with $Z \geq 8$ arXiv Paper Yun-Dong Wang, Tian-Shuai Shang et al. Apr 7 007 Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics arXiv Paper Brandon B. Le, D. Keller Jan 19 008 Extrapolation to infinite model space of no-core shell model calculations using machine learning arXiv Paper A. Mazur, Roman O. Sharypov et al. Nov 7, 2025 009 Quantum Dynamics with Time-Dependent Neural Quantum States arXiv Paper Alejandro Romero-Ros et al. Sep 29, 2025 010 Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation arXiv Paper Christopher Brady et al. Sep 9, 2025 011 Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data arXiv Paper K. Fukushima, Syo Kamata Aug 27, 2025 012 Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data arXiv Paper M. Imbrišak et al. Aug 26, 2025 013 The DNA of nuclear models: How AI predicts nuclear masses arXiv Paper Kate A. Richardson et al. Aug 11, 2025 014 Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data arXiv Paper B. E. Kowal, K. Graczyk et al. Aug 1, 2025 015 Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification arXiv Paper Daniel Sadasivan et al. Jul 24, 2025 016 DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States arXiv Paper Wei-Lin Wu, L. Meng et al. Jun 25, 2025 017 Training neural control variates using correlated configurations arXiv Paper Hyunwoo Oh May 12, 2025 018 Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics arXiv Paper Jonathan Gorard, Ammar Hakim et al. May 5, 2025 019 Compton Form Factor Extraction using Quantum Deep Neural Networks arXiv Paper Brandon B. Le, Dustin Keller Apr 21, 2025 020 Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF arXiv Paper Sascha Caron, Andreas Ipp et al. Mar 18, 2025 021 Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model arXiv Paper I. Bentley, J. Tedder et al. Mar 14, 2025 022 An Efficient Learning Method to Connect Observables arXiv Paper Hang Yu et al. Mar 3, 2025 023 Generative adversarial neural networks for simulating neutrino interactions arXiv Paper J. L. Bonilla, K. Graczyk et al. Feb 27, 2025 024 Global Framework for Emulation of Nuclear Calculations arXiv Paper Antoine Belley et al. Feb 27, 2025 025 Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics arXiv Paper Gert Aarts et al. Jan 9, 2025 026 Machine Learning Neutrino-Nucleus Cross Sections arXiv Paper Daniel C. Hackett, Joshua Isaacson et al. Dec 20, 2024 027 Sim-to-real supervised domain adaptation for radioisotope identification arXiv Paper Peter Lalor et al. Dec 10, 2024 028 Electron-nucleus cross sections from transfer learning arXiv Paper K. Graczyk, B. E. Kowal et al. Aug 19, 2024 029 From Neurons to Neutrons: A Case Study in Interpretability arXiv Paper O. Kitouni, N. S. Nolte et al. May 27, 2024 030 Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis arXiv Paper Pablo Giuliani et al. May 17, 2024 031 Discovering Nuclear Models from Symbolic Machine Learning arXiv Paper Jose M. Munoz, S. Udrescu et al. Apr 17, 2024 032 Parametric Matrix Models arXiv Paper Patrick Cook et al. Jan 22, 2024 033 Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods arXiv Paper B. E. Kowal, K. Graczyk et al. Dec 28, 2023 034 Leveraging neural control variates for enhanced precision in lattice field theory arXiv Paper Paulo F. Bedaque et al. Dec 13, 2023 035 Quantum-classical simulation of quantum field theory by quantum circuit learning arXiv Paper Kazuki Ikeda Nov 27, 2023 036 Local Bayesian Dirichlet mixing of imperfect models arXiv Paper Vojtech Kejzlar, Léo Neufcourt et al. Nov 2, 2023 037 Lattice real-time simulations with learned optimal kernels arXiv Paper Daniel Alvestad et al. Oct 12, 2023 038 Artificial Intelligence for the Electron Ion Collider (AI4EIC) arXiv Paper C. Allaire, R. Ammendola et al. Jul 17, 2023 039 Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma arXiv Paper Kevin Li, Simon Mak et al. Jun 11, 2023 040 NuCLR: Nuclear Co-Learned Representations arXiv Paper Ouail Kitouni et al. Jun 9, 2023 041 Predicting nuclear masses with product-unit networks arXiv Paper Babette Dellen et al. May 8, 2023 042 An introduction to computational complexity and statistical learning theory applied to nuclear models arXiv Paper A. Idini Nov 11, 2022 043 Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis arXiv Paper Paul Lartaud et al. Nov 4, 2022 044 Efficient Learning of Accurate Surrogates for Simulations of Complex Systems arXiv Paper A. Diaw, Michael McKerns et al. Jul 11, 2022 045 Application of multilayer perceptron with data augmentation in nuclear physics arXiv Paper Huseyin Bahtiyar, Derya Soydaner et al. May 16, 2022 046 New insights into four-boson renormalization group limit cycles arXiv Paper Bastian Kaspschak et al. Mar 28, 2022 047 Machine Learning in Nuclear Physics arXiv Paper A. Boehnlein, M. Diefenthaler et al. Dec 4, 2021 048 Machine learning Hadron Spectral Functions in Lattice QCD arXiv Paper Shiyang Chen, H. Ding et al. Dec 1, 2021 049 Three-body renormalization group limit cycles based on unsupervised feature learning arXiv Paper Bastian Kaspschak, U. Meissner Nov 15, 2021 050 Machine learning spectral functions in lattice QCD arXiv Paper S.-Y. Chen, H. Ding et al. Oct 26, 2021 051 Self-learning Emulators and Eigenvector Continuation arXiv Paper Avik Sarkar et al. Jul 28, 2021 052 Shared Data and Algorithms for Deep Learning in Fundamental Physics arXiv Paper L. Benato, E. Buhmann et al. Jul 1, 2021 053 Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation arXiv Paper Yuki Fujimoto, K. Fukushima et al. Jan 20, 2021 054 A Neural Network Perturbation Theory Based on the Born Series arXiv Paper Bastian Kaspschak, U. Meissner Sep 7, 2020 055 Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning arXiv Paper Yu. Kvasiuk, E. Zabrodin et al. Apr 29, 2020 056 Trees and Forests in Nuclear Physics arXiv Paper Marco Carnini et al. Feb 24, 2020 057 Statistical aspects of nuclear mass models arXiv Paper Vojtech Kejzlar et al. Feb 11, 2020 058 Quantified limits of the nuclear landscape arXiv Paper Léo Neufcourt et al. Jan 16, 2020 059 Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei arXiv Paper Léo Neufcourt et al. Oct 28, 2019 060 Trees and Islands -- Machine learning approach to nuclear physics arXiv Paper Nishchal R. Dwivedi Jul 23, 2019