Copertina

Exterior complex scaling enables physics-informed neural networks for quantum scattering

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Lei, Jin
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Nuclear Theory Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Addressing the ground state of the deuteron by physics-informed neural networks
di: Brevi, Lorenzo, et al.
Pubblicazione: (2026)

HPRMAT: A high-performance R-matrix solver with GPU acceleration for coupled-channel problems in nuclear physics
di: Lei, Jin
Pubblicazione: (2025)

COLOSS: Complex-scaled Optical and couLOmb Scattering Solver
di: Liu, Junzhe, et al.
Pubblicazione: (2024)

Light nuclear scattering from neural quantum states
di: Lawrence, Scott, et al.
Pubblicazione: (2026)

Modeling inclusive electron-nucleus scattering with Bayesian artificial neural networks
di: Sobczyk, Joanna E., et al.
Pubblicazione: (2024)

Validation and extrapolation of atomic mass with physics-informed fully connected neural network
di: Huang, Yiming, et al.
Pubblicazione: (2025)

Second-order optimisation strategies for neural network quantum states
di: Drissi, M., et al.
Pubblicazione: (2024)

Machine learning one-dimensional spinless trapped fermionic systems with neural-network quantum states
di: Keeble, J. W. T., et al.
Pubblicazione: (2023)

A neural network approach for two-body systems with spin and isospin degrees of freedom
di: Wang, Chuanxin, et al.
Pubblicazione: (2024)

Highly efficient nuclear population transfer through physics-informed neural networks
di: Liu, Jing, et al.
Pubblicazione: (2025)

Neural-network quantum states for solving few-body problems: application to Efimov physics
di: Yokoi, Sora, et al.
Pubblicazione: (2026)

Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
di: Li, Fu-Peng, et al.
Pubblicazione: (2026)

Achieving angular-momentum conservation with physics-informed neural networks in computational relativistic spin hydrodynamics
di: Matsuda, Hidefumi, et al.
Pubblicazione: (2025)

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

Nuclear scattering via quantum computing
di: Wang, Peiyan, et al.
Pubblicazione: (2024)

Elastic scattering on a quantum computer
di: Yusf, Muhammad, et al.
Pubblicazione: (2024)

Studying few cluster resonances with quantum neural network driven iterative Harrow-Hassidim-Lloyd algorithm
di: Zhang, Hantao, et al.
Pubblicazione: (2025)

Proton-proton scattering on a quantum computer
di: Khadka, Ratna, et al.
Pubblicazione: (2025)

Geometric phase from encircling an exceptional point of a quantum resonance in the complex-scaling method
di: Morikawa, Okuto, et al.
Pubblicazione: (2025)

Physics-informed neural networks for angular-momentum conservation in computational relativistic spin hydrodynamics
di: Matsuda, Hidefumi, et al.
Pubblicazione: (2026)

Fixed-energy inverse scattering with radial basis function neural networks and its application to neutron-alpha interactions
di: Balassa, Gábor
Pubblicazione: (2026)

Nuclear responses with neural-network quantum states
di: Parnes, Elad, et al.
Pubblicazione: (2025)

Reduced basis emulator for elastic scattering in continuum-discretized coupled-channel calculations
di: Lei, Jin
Pubblicazione: (2025)

Evaluation of phase shifts for non-relativistic elastic scattering using quantum computers
di: Turro, Francesco, et al.
Pubblicazione: (2024)

Architecture as physical prior: cooperative neural network for nuclear masses
di: Zai, Peiwen, et al.
Pubblicazione: (2026)

A deep neural network approach to solve the Dirac equation
di: Wang, Chuanxin, et al.
Pubblicazione: (2024)

Machine learning the single-$Λ$ hypernuclei with neural-network quantum states
di: Zhang, Zi-Xiao, et al.
Pubblicazione: (2025)

Nuclear configurational complexity in high-energy hadron-hadron scatterings
di: Karapetyan, G.
Pubblicazione: (2025)

Efficient and precise quantum simulation of ultra-relativistic quark-nucleus scattering
di: Wu, Sihao, et al.
Pubblicazione: (2024)

Constraints on new physics with (anti)neutrino-nucleon scattering data
di: Tomalak, Oleksandr, et al.
Pubblicazione: (2024)

Scale-(in)dependence in quantum 4-body scattering
di: Mondal, Sourav, et al.
Pubblicazione: (2024)

Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state
di: Thete, Ameya, et al.
Pubblicazione: (2022)

Visualization of quantum interferences in heavy-ion elastic scattering
di: Heo, Kyoungsu, et al.
Pubblicazione: (2025)

Applied nonrelativistic conformal field theory: scattering-length and effective-range corrections to unnuclear physics
di: Chowdhury, Subham Dutta, et al.
Pubblicazione: (2023)

Surrogate Models for Linear Response
di: Jin, L., et al.
Pubblicazione: (2025)

Neural-network quantum states for the nuclear many-body problem
di: Lovato, Alessandro, et al.
Pubblicazione: (2026)

Branching ratio and information entropy for the p+$^{12}$C reaction in the extended-quantum-molecular-dynamics model
di: Shen, Lei, et al.
Pubblicazione: (2025)

Robust extrapolation using physics-related activation functions in neural networks for nuclear masses
di: Kim, C. H., et al.
Pubblicazione: (2025)

Criticality analysis of nuclear binding energy neural networks
di: Sundberg, S. A., et al.
Pubblicazione: (2025)

Solving the QCD effective kinetic theory with neural networks
di: Cabodevila, Sergio Barrera, et al.
Pubblicazione: (2025)