Physics-constrained neural networks for surrogate modeling of lossless periodic structures — Paper

Abstract

-cross Abstract: We introduce a physics-constrained neural network for the rapid prediction of rigorous coupled-wave analysis outputs in the form of Jones matrices. Starting from energy conservation in lossless layered periodic structures, we use the fact that the scattering outputs lie on a Stiefel manifold. This energy constraint is enforced as a hard condition by projecting onto the manifold using differentiable symmetric orthogonalization. The resulting surrogate enforces energy conservation by construction while preserving differentiability for gradient-based inverse design. The performance and generality of the proposed approach are demonstrated through the inverse design of a diffractive waveguide combiner for augmented reality glasses.

Authors 2

Eric Prehn, Peter Jung

Specification

Official page: arxiv.org/abs/2606.28119
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
arXiv id: 2606.28119
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Categories: physics.optics, cs.LG
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
PDF: arxiv.org/pdf/2606.28119
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Primary category: physics.optics
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh

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9

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T19

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8 h ago

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Authors: Eric Prehn, Peter Jung

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9 claims · 9 properties

Official pageofficial_url1

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https://arxiv.org/abs/2606.28119	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Abstractabstract1

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-cross Abstract: We introduce a physics-constrained neural network for the rapid prediction of rigorous coupled-wave analysis outputs in the form of Jones matrices. Starting from energy conservation in lossless layered periodic structures, we use the fact that the scattering outputs lie on a Stiefel manifold. This energy constraint is enforced as a hard condition by projecting onto the manifold using differentiable symmetric orthogonalization. The resulting surrogate enforces energy conservation by construction while preserving differentiability for gradient-based inverse design. The performance and generality of the proposed approach are demonstrated through the inverse design of a diffractive waveguide combiner for augmented reality glasses.	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Arxiv announce typearxiv_announce_type1

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replace	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

arXiv idarxiv_id1

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2606.28119	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Authorsauthors1

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Eric Prehn, Peter Jung	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Categoriescategories1

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Value	Valid from → to	Status	Source	Confidence	Extractor
physics.optics, cs.LG	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

PDFpdf_url1

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https://arxiv.org/pdf/2606.28119	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Primary categoryprimary_category1

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Value	Valid from → to	Status	Source	Confidence	Extractor
physics.optics	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Publishedpublished_at1

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Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperPhysics-constrained neural networks for surrogate modeling of lossless periodic structures
New paper: Physics-constrained neural networks for surrogate modeling of lossless periodic structures
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	6 h ago	1

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