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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 totraditional Artificial Neural Networks (ANNs). We first extend the calibrationtechnique of SNNs to arbitrary activation functions beyond ReLU, making it moreversatile, and we prove a theorem that ensures the effectiveness of thecalibration. We successfully convert PINNs to SNNs, enabling computationalefficiency for diverse regression tasks in solving multiple differentialequations, including the unsteady Navier-Stokes equations. We demonstrate greatgains in terms of overall efficiency, including Separable PINNs (SPINNs), whichaccelerate the training process. Overall, this is the first work of this kindand the proposed method achieves relatively good accuracy with low spike rates.

Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning