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Results for “INTERPOLATION”
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Excited-state effects in nucleon structure on the lattice using hybrid interpolators
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Interpolating ’t Hooft model between instant and front forms
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Strategies for quantum-optimized construction of interpolating operators in classical simulations of lattice quantum field theories
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Two-dimensional transverse charge distributions of the Δ baryon: Interpolation between the nonrelativistic and ultrarelativistic limits
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Interpolating quantum electrodynamics between instant and front forms
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Accelerating Lattice Quantum Field Theory Calculations via Interpolator Optimization Using Noisy Intermediate-Scale Quantum Computing
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Interpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications
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Interpolated kilonova spectra models: Examining the effects of a phenomenological, blue component in the fitting of AT2017gfo spectra
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Kilonova light-curve interpolation with neural networks
Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024
Stable Computation of Generalized Matrix Functions via Polynomial Interpolation
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Recursive, Parameter-Free, Explicitly Defined Interpolation Nodes for Simplices
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Spectroscopy and Scattering Studies Using Interpolated Ab Initio Potentials
The Born–Oppenheimer potential energy surface (PES) has come a long way since its introduction in the 1920s, both conceptually and in predictive power for practical applications. Nevertheless, nearly 100 years later—despite astonishing advances in computational power—the state-of-the-art first-principles prediction of observables related to spectroscopy and scattering dynamics is surprisingly limited. For example, the water dimer, (H 2 O) 2 , with only six nuclei and 20 electrons, still presents a formidable challenge for full-dimensional variational calculations of bound states and is considered out of reach for rigorous scattering calculations. The extremely poor scaling of the most rigorous quantum methods is fundamental; however, recent progress in development of approximate methodologies has opened the door to fairly routine high-quality predictions, unthinkable 20 years ago. In this review, in relation to the workflow of spectroscopy and/or scattering studies, we summarize progress and challenges in the component areas of electronic structure calculations, PES fitting, and quantum dynamical calculations.
A Statistical Interpolation Code for Ocean Analysis and Forecasting
Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .
Dewarping Algorithm Comparison: Local vs. Global 2D Polynomial Interpolation for Dewarping
This is a short report for collaborators in the radiography working group related to Cygnus radiography.
Interpolation data transfer between the models before and after a partial drawdown leach in BH
It has been recognized that as cavern operations become more frequent due to oil sales, field conditions may arise which require a faster turnaround time of analysis to address potential cavern impacts. This letter describes attempts to implement a strategy of transferring an intermediate solution of a Big Hill (BH) geomechanical model from a previous finite element mesh with a specified cavern geometry, to a new mesh with a new cavern geometry created by leaching from an oil sale operation.
Conservative Remap and Interpolation for the Discrete Element for Sea Ice Model.
Abstract not provided.