Supplementary Material for: Benchmarking Physics-Informed Neural Networks Against Boundary Element Methods for Wave Scattering: Accuracy–Efficiency Trade-offs

This supplementary material provides additional methodological details supporting the analyses presented in the main manuscript.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-23
DOI
https://doi.org/10.5281/zenodo.18352233
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

Supplementary Material for: Benchmarking Physics-Informed Neural Networks Against Boundary Element Methods for Wave Scattering: Accuracy–Efficiency Trade-offs

Silvana Montoya‐Noguera, Gregorio Pérez-Bernal, Nicolás Guarín-Zapata, Oscar A. Rincón
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Supplementary Material for: Benchmarking Physics-Informed Neural Networks Against Boundary Element Methods for Wave Scattering: Accuracy–Efficiency Trade-offs

Silvana Montoya‐Noguera, Gregorio Pérez-Bernal, Nicolás Guarín-Zapata, Oscar A. Rincón
article en

Abstract

This supplementary material provides additional methodological details supporting the analyses presented in the main manuscript.

Zenodo (CERN European Organization for Nuclear Research)
Universidad EAFIT (CO)
Openalex Percentile: Top 78%
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Supplementary Material for: Benchmarking Physics-Informed Neural Networks Against Boundary Element Methods for Wave Scattering: Accuracy–Efficiency Trade-offs — Silvana Montoya‐Noguera, Gregorio Pérez-Bernal, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS