Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
Mixed-precision numerics in scientific applications: survey and perspectives
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PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
Empirical tests show mixed-precision Bogacki-Shampine 3(2) Runge-Kutta preserves most high-precision accuracy on large ODE systems like Kuramoto and circadian models, with accuracy improving at larger scales.
citing papers explorer
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
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Floating-point autotuning with customized precisions
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
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Mixed-Precision in adaptive Runge-Kutta method for large ODE systems
Empirical tests show mixed-precision Bogacki-Shampine 3(2) Runge-Kutta preserves most high-precision accuracy on large ODE systems like Kuramoto and circadian models, with accuracy improving at larger scales.