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Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning

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arxiv 2308.16372 v1 pith:L6V5G6XF submitted 2023-08-31 cs.NE

Qian Zhang , Chenxi Wu , Adar Kahana , Youngeun Kim , Yuhang Li , George Em Karniadakis , Priyadarshini Panda This is my paper · ORCID
classification cs.NE
keywords networksneuralefficiencypinnssnnsartificialcalibrationconvert
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We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs). We first extend the calibration technique of SNNs to arbitrary activation functions beyond ReLU, making it more versatile, and we prove a theorem that ensures the effectiveness of the calibration. We successfully convert PINNs to SNNs, enabling computational efficiency for diverse regression tasks in solving multiple differential equations, including the unsteady Navier-Stokes equations. We demonstrate great gains in terms of overall efficiency, including Separable PINNs (SPINNs), which accelerate the training process. Overall, this is the first work of this kind and the proposed method achieves relatively good accuracy with low spike rates.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Hybrid variable spiking graph neural networks match vanilla GNN accuracy on three mechanics regression tasks while reducing spike-based communication.

  2. Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach

    physics.flu-dyn 2025-01 conditional novelty 5.0 of 10

    An Energy Transformer reconstructs full flow fields from patch-masked observations with only 10% of patches visible, achieving relative errors of 0.04 to 0.27 across three fluid mechanics datasets.

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