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New SEA-PINN architecture enhances physics-informed neural networks

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced SEA-PINN, a new architecture that integrates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks. This addition helps to dynamically adjust the importance of neurons across layers, leading to more stable initialization and reduced initial loss on benchmark problems. SEA-PINN demonstrates competitive accuracy, even outperforming specialized models on high-frequency problems and significantly boosting performance when integrated with existing architectures. AI

IMPACT This new architecture could lead to more robust and efficient convergence in physics-informed learning tasks.

RANK_REASON The cluster contains a research paper detailing a new model architecture.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SEA-PINN architecture enhances physics-informed neural networks

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The cluster contains a research paper detailing a new model architecture.
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2 independent sources
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paper, model release
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127 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yun-Fei Song, Long-Gang Pang, Fu-Peng Li, Jun-Jie Zhang ·

    Physics-Informed Neural Network with Squeeze-Excitation-like Attention

    arXiv:2606.19853v1 Announce Type: new Abstract: We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the importance of neurons across layers. A key feature of S…

  2. arXiv cs.LG TIER_1 English(EN) · Jun-Jie Zhang ·

    Physics-Informed Neural Network with Squeeze-Excitation-like Attention

    We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the importance of neurons across layers. A key feature of SEA-PINN is its highly stable initialization. On …

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