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arXiv · 2502.04917

Complex Physics-Informed Neural Network

Chenhao Si·Ming Yan·Xin Li·Zhihong Xia
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Abstract

We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer. Empirically, we demonstrate that compleX-PINN solves high-dimensional problems that pose significant challenges for PINNs. Our results show that compleX-PINN consistently achieves substantially greater precision, often improving accuracy by an order of magnitude, on these complex tasks.

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Chenhao Si, Ming Yan, Xin Li, Zhihong Xia. 2025-02-07. Complex Physics-Informed Neural Network. https://arxiv.org/abs/2502.04917

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