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DeepONet learns Helmholtz equation operator for non-parametric 2D geometries

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a physics-informed neural operator network, DeepONet, to solve the 2D Helmholtz equation on non-parametric domains. This approach learns the relationship between a scatterer's geometry and the resulting wave field, using a signed distance function to encode arbitrary shapes. The model offers a computationally lighter alternative to traditional finite element methods and avoids the need for domain remeshing. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Offers a computationally lighter surrogate model for solving complex physics equations, potentially accelerating simulation and design processes.

RANK_REASON This is a research paper detailing a novel application of neural networks to solve a specific physics equation.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz, St\'ephane Grieu ·

    Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries

    arXiv:2605.00760v1 Announce Type: new Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the DeepONet framework. We consider a 2D square domain with an inclusion of arbitrary …

  2. arXiv cs.LG TIER_1 · Stéphane Grieu ·

    Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries

    This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the DeepONet framework. We consider a 2D square domain with an inclusion of arbitrary boundary geometry at its center. This inclusion …

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