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Asymptotics-guided learning and symbolic regression for dispersive resonances — arXiv:2608.16152

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16152
License (as archived): CC0-1.0
Archived: 2026-10-18T19:44:20.837Z
SHA-256 of the archived text: 2cd93d5039f5035f83b71587a6c58eff657ac402ab6b8d8e0895bbaab6fa3cd7
Stable URL of this page: https://metraipolis.com/documento/dc_d6dbddeaeed6

How to cite

Asymptotics-guided learning and symbolic regression for dispersive resonances — arXiv:2608.16152 — arXiv (en), https://arxiv.org/abs/2608.16152 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d6dbddeaeed6 (Metraipolis archive dc_d6dbddeaeed6, sha256 2cd93d5039f5035f83b71587a6c58eff657ac402ab6b8d8e0895bbaab6fa3cd7)

BibTeX

@misc{dcd6dbddeaeed6,
  title = {Asymptotics-guided learning and symbolic regression for dispersive resonances — arXiv:2608.16152},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16152},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d6dbddeaeed6; sha256 2cd93d5039f5035f83b71587a6c58eff657ac402ab6b8d8e0895bbaab6fa3cd7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16152. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study resonance prediction in dispersive media, formulated as nonlinear spectral problems for volume integral operators. The main idea is to use asymptotic analysis not only as a baseline approximation, but also as a guide for constructing predictive correction models. We learn the residual between asymptotic and reference resonances using features suggested by the subwavelength expansion, including the logarithmic scales specific to two dimensions. The resulting corrections substantially improve single-resonator and dimer predictions, and symbolic regression produces compact formulas for the learned residual. The results show that asymptotic analysis can be used not only to approximate resonances, but also to design the feature space in which data-driven corrections become accurate, low-dimensional, and interpretable.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.LG).
Categorie arXiv: Numerical Analysis (math.NA) ; Machine Learning (cs.LG); Mathematical Physics (math-ph); Analysis of PDEs (math.AP); Optics (physics.optics).
Pagina del lavoro: https://arxiv.org/abs/2608.16152 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T19:02:44.299Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).