mlsanityData to 7 Oct 2026

Interpretable Analytic Calabi-Yau Metrics via Symbolic Distillation

arXiv 2602.07834 ↗First posted 8 Feb 2026cs.LG math.DGcited by 1

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Abstract (arXiv)

The pointwise determinant ratio [ R_psi(z)equiv log!left(fracdet gRF(z;psi)det gFS(z)right) ] measures how the Ricci-flat metric on the Dwork quintic departs from the Fubini--Study baseline. We ask whether this scalar observable can be described compactly in terms of a small number of projective invariants, and whether the same scaffold remains usable across complex-structure moduli. Using Donaldson's k=10 balanced metric as an algebraic teacher and symbolic regression on sampled points, we find that, within the restricted moduli-only feature class studied here, two low-order symmetric features, the power sum p2=sumi |zi|4 and the cubic elementary symmetric polynomial σ3=e3, already capture most of the teacher variation. A degree-3 polynomial in (p2,σ3) achieves held-out test R2=0.946, while adding the remaining low-order symmetric generators changes this by less than 10-3. Within the same two-feature space, symbolic regression identifies a five-term rational-polynomial expression that matches the k=10 teacher with R2=0.9994. Refitting the same functional scaffold across psi∈[0,0.8] keeps the mean determinant-ratio proxy langle R_psirangle within 0.01% of the local teachers on the sampled point clouds and yields smoothly varying fitted coefficients over the studied range. The holomorphic Yukawa coupling kappa111=5 is reproduced as a normalization check only. Taken together, these results provide a compact symbolic description of one metric-derived scalar observable on the Dwork family, while remaining bounded by the finite-k teacher used for distillation rather than establishing a closed-form Ricci-flat metric.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 45 references that are in the map. First posted on arXiv 8 Feb 2026.

Builds on

13 of 45 references.

References · 13

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 536
  3. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 335
  4. 2020Neural Network Approximations for Calabi-Yau Metricscited by 32
  5. 2021Learning Size and Shape of Calabi-Yau Spacescited by 28
  6. 2019Fast, accurate, and transferable many-body interatomic potentials by symbolic regressioncited by 23
  7. 2019Machine learning Calabi-Yau metricscited by 23
  8. 2023Distill n' Explain: explaining graph neural networks using simple surrogatescited by 18
  9. 2022Machine Learned Calabi-Yau Metrics and Curvaturecited by 14
  10. 2024Discovering physical laws with parallel symbolic enumerationcited by 9
  11. 2024Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithmcited by 4
  12. 2024Symbolic Approximations to Ricci-flat Metrics Via Extrinsic Symmetries of Calabi-Yau Hypersurfacescited by 4
1 more
  1. 2025Approximate Ricci-flat Metrics for Calabi-Yau Manifoldscited by 1

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