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This paper argues that curvelets provide a powerful tool for representing very general linear symmetric systems of hyperbolic differential equations. Curvelets are a recently developed multiscale system in which the elements are highly…

Analysis of PDEs · Mathematics 2007-05-23 Emmanuel J. Candes , Laurent Demanet

Gravitational wave detector technology provides high-precision measurement apparatuses that, if combined with a modulated particle source, have the potential to measure and constrain particle interactions in a novel way, by measuring the…

High Energy Physics - Phenomenology · Physics 2018-10-29 Christoph Englert , Stefan Hild , Michael Spannowsky

We present a basis-set-free approach to the variational quantum eigensolver using an adaptive representation of the spatial part of molecular wavefunctions. Our approach directly determines system-specific representations of qubit…

Quantum Physics · Physics 2021-01-05 Jakob S. Kottmann , Philipp Schleich , Teresa Tamayo-Mendoza , Alán Aspuru-Guzik

Visual representations are defined in terms of minimal sufficient statistics of visual data, for a class of tasks, that are also invariant to nuisance variability. Minimal sufficiency guarantees that we can store a representation in lieu of…

Computer Vision and Pattern Recognition · Computer Science 2016-06-29 Stefano Soatto , Alessandro Chiuso

A summary of recent researches on nuclear dynamics with realistic microscopic quantum approaches is presented. The Balian-V\'en\'eroni variational principle is used to derive the time-dependent Hartree-Fock (TDHF) equation describing the…

Nuclear Theory · Physics 2014-06-03 Cédric Simenel

Selected resonance states of the deuterated formyl radical in the electronic ground state ($\widetilde{X}\,{}^2A'$) are computed using our recently introduced dynamically pruned discrete variable representation (DP-DVR) [H. R. Larsson, B.…

Chemical Physics · Physics 2018-07-04 Henrik R. Larsson , Jens Riedel , Jie Wei , Friedrich Temps , Bernd Hartke

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in…

Machine Learning · Computer Science 2022-03-25 Jeremiah Birrell , Markos A. Katsoulakis , Yannis Pantazis

Low-lying shell model states may be approximated accurately by a sum over products of proton and neutron states. The optimal factors are determined by a variational principle and result from the solution of rather low-dimensional eigenvalue…

Nuclear Theory · Physics 2007-05-23 T. Papenbrock , A. Juodagalvis , D. J. Dean

Reinforcement learning (RL) agents make decisions using nothing but observations from the environment, and consequently, heavily rely on the representations of those observations. Though some recent breakthroughs have used vector-based…

Machine Learning · Computer Science 2024-07-16 Edan Meyer , Adam White , Marlos C. Machado

An unusual regime for liquid-state nuclear magnetic resonance (NMR) where the magnetic field strength is so low that the $J$-coupling (intramolecular spin-spin) interactions dominate the spin Hamiltonian opens a new paradigm with…

Atomic Physics · Physics 2019-10-02 Dmitry Budker

Because of the rotational invariance of the nuclear Hamiltonian, there exists a density functional for nuclei that depends only on two scalar densities. Practical calculations boil down to radial, one-dimensional ones.

Nuclear Theory · Physics 2008-11-26 B. G. Giraud

Photonic quantum computation refers to quantum computation that uses photons as the physical system for doing the quantum computation. The field is largely divided between discrete-variable (DV) and continuous-variable (CV) photonic quantum…

Quantum Physics · Physics 2025-10-10 Jacquiline Romero , Gerard Milburn

We analyze the anti-symmetric properties of a spectral discretization for the one-dimensional Vlasov-Poisson equations. The discretization is based on a spectral expansion in velocity with the symmetrically weighted Hermite basis functions,…

Numerical Analysis · Mathematics 2024-10-31 Opal Issan , Oleksandr Koshkarov , Federico D. Halpern , Boris Kramer , Gian Luca Delzanno

The variation after projection (VAP) method is expected to be an efficient way of getting the optimized nuclear wave functions, so that they can be as close as possible to the exact shell model ones. However, we found there are two…

Nuclear Theory · Physics 2023-06-21 Xiao Lu , Zhan-Jiang Lian , Xue-Wei Li , Zao-Chun Gao , Yong-Shou Chen

Covariant density functional theory, which has so far been applied only within the framework of static and time dependent mean field theory is extended to include Particle-Vibration Coupling (PVC) in a consistent way. Starting from a…

Nuclear Theory · Physics 2008-11-26 E. Litvinova , P. Ring , V. Tselyaev

Applications of variational methods are typically restricted to conservative systems. Some extensions to dissipative systems have been reported too but require ad hoc techniques such as the artificial doubling of the dynamical variables.…

Plasma Physics · Physics 2017-04-05 I. Y. Dodin , A. I. Zhmoginov , D. E. Ruiz

We present a new realization of relativistic hadronic me- chanics and its underlying iso-Poincar'e symmetry specifically constructed for nuclear physics which: 1) permits the representation of nucleons as ex- tended, nonspherical and…

General Physics · Physics 2008-02-03 Ruggero Maria Santilli

Applications of relativistic light front dynamics to computing wave functions of heavy nuclei are reviewed. The motivation for this is the desire to find wave functions, expressed in terms of the plus-momentum variable, that simplify the…

Nuclear Theory · Physics 2011-08-11 Gerald A. Miller

The representation of the usual integral dispersion relations (IDR) of scattering theory through series of derivatives of the amplitudes is discussed, extended, simplified, and confirmed as mathematical identities. Forms of derivative…

High Energy Physics - Phenomenology · Physics 2008-11-26 Erasmo Ferreira , Javier Sesma

Advantageous numerical methods for solving the Dirac equations are derived. They are based on different stochastic optimization techniques, namely the Genetic algorithms, the Particle Swarm Optimization and the Simulated Annealing method,…

Computational Physics · Physics 2019-02-20 Ioannis G. Tsoulos , O. T. Kosmas , V. N. Stavrou
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