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Knots, minimal surfaces and J-holomorphic curves

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arxiv 2112.07713 v3 pith:W33RSOFA submitted 2021-12-14 math.DG math.GTmath.SG

Knots, minimal surfaces and J-holomorphic curves

classification math.DG math.GTmath.SG
keywords mathbbinvariantsboundaryknotdiscsfactgromov--wittenholomorphic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Let $K$ be a knot in the 3-sphere, viewed as the ideal boundary of hyperbolic 4-space $\mathbb{H}^4$. We prove that the number of minimal discs in $\mathbb{H}^4$ with ideal boundary $K$ is a knot invariant. I.e.\ the number is finite and doesn't change under isotopies of $K$. In fact this gives a family of knot invariants, indexed by an integer describing the extrinsic topology of how the disc sits in $\mathbb{H}^4$. These invariants can be seen as Gromov--Witten invariants counting $J$-holomorphic discs in the twistor space $Z$ of $\mathbb{H}^4$. Whilst Gromov--Witten theory suggests the general scheme for defining the invariants, there are substantial differences in how this must be carried out in our situation. These are due to the fact that the geometry of both $\mathbb{H}^4$ and $Z$ becomes singular at infinity, and so the $J$-holomorphic curve equation is degenerate, rather than elliptic, at the boundary. This means that both the Fredholm and compactness arguments involve completely new features.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Minimal surfaces, Knots, and Neural Networks

    math.DG 2026-05 unverdicted novelty 7.0

    PINNs approximate near-minimal surfaces bounding knots in S^3; their self-intersection numbers align with Fine's conjecture predictions derived from the HOMFLY polynomial.

  2. A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem

    math.DG 2026-07 conditional novelty 5.0

    Hard-encoding boundary geometry and propagating second-order jets with a compiled graph makes a PINN for the asymptotic Plateau problem 40–50 times faster per training step.