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.
Woods and David S
3 Pith papers cite this work, alongside 820 external citations. Polarity classification is still indexing.
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Three standard inflationary potentials remain compatible with Planck, BICEP/Keck, DESI DR2, and ACT DR6 data when placed in minimally coupled f(R,T)=R+16πGλT gravity for suitable ranges of the model parameters and coupling λ.
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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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Inflationary models in a minimally coupled $f(R,T)$ gravity: Constraints from $Planck$, BICEP/$Keck$, and ACT
Three standard inflationary potentials remain compatible with Planck, BICEP/Keck, DESI DR2, and ACT DR6 data when placed in minimally coupled f(R,T)=R+16πGλT gravity for suitable ranges of the model parameters and coupling λ.
- Determination of Density Functional Tight Binding Models for Cerium-containing Materials