Explicit 11D solutions show flux stabilization of T^4/Z2 moduli in EFT does not match the full theory, with non-Lorentz-invariant deformations stabilizing a mix of volume and shape moduli instead.
Numerical Calabi–Yau metrics from holomorphic networks
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.
PINNs can address differential geometry problems by training neural networks to minimize functionals that encode geometric conditions, as shown through summaries of three related studies.
citing papers explorer
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Lost in Translation: Moduli Stabilization from EFT to Eleven Dimensions
Explicit 11D solutions show flux stabilization of T^4/Z2 moduli in EFT does not match the full theory, with non-Lorentz-invariant deformations stabilizing a mix of volume and shape moduli instead.
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Minimising Willmore Energy via Neural Flow
Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.
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PINNs in More General Geometry
PINNs can address differential geometry problems by training neural networks to minimize functionals that encode geometric conditions, as shown through summaries of three related studies.