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PINNs struggle with noisy data compared to traditional methods, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates the effectiveness of Physics-Informed Neural Networks (PINNs) when dealing with noisy data in inverse problems. The study found that while PINNs may require less specialized knowledge, traditional methods like the finite element method generally outperform them in accuracy for solving partial differential equations. However, PINNs show better scalability with problem complexity and require further development to address training failures and improve competitiveness with noisy data. AI

IMPACT PINNs require further development to become competitive with traditional methods for inverse problems involving noisy data.

RANK_REASON Research paper published on arXiv detailing findings on the performance of a specific machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PINNs struggle with noisy data compared to traditional methods, study finds

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Research paper published on arXiv detailing findings on the performance of a specific machine learning technique. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-S{\o}rensen, Helge Langseth, Odd Erik Gundersen ·

    Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems

    arXiv:2509.20191v2 Announce Type: replace-cross Abstract: Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineering. Physics-informed neural networks (PINNs) are a recent machine learning-based…

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