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
2026-04-20
DOI
https://doi.org/10.5281/zenodo.19673753
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

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

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

Gregorio Pérez-Bernal, Nicolás Guarín-Zapata, Oscar A. Rincón, Silvana Montoya-Noguera
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 6%
Model Reduction and Neural Networks
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Supplementary Material for: Benchmarking Physics-Informed Neural Networks Against Boundary Element Methods for Wave Scattering: Accuracy–Efficiency Trade-offs — Gregorio Pérez-Bernal, Nicolás Guarín-Zapata, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS