More Consistent Accuracy PINN via Alternating Easy-Hard Training
This work addresses reliability issues in PINNs for solving PDEs, offering a hybrid training method that improves consistency, though it is incremental as it builds on existing prioritization approaches.
The paper tackles inconsistent performance in physics-informed neural networks (PINNs) across different PDE types by developing an alternating easy-hard training strategy, achieving relative L2 errors mostly in O(10^-5) to O(10^-6) and surpassing baseline methods.
Physics-informed neural networks (PINNs) have recently emerged as a prominent paradigm for solving partial differential equations (PDEs), yet their training strategies remain underexplored. While hard prioritization methods inspired by finite element methods are widely adopted, recent research suggests that easy prioritization can also be effective. Nevertheless, we find that both approaches exhibit notable trade-offs and inconsistent performance across PDE types. To address this issue, we develop a hybrid strategy that combines the strengths of hard and easy prioritization through an alternating training algorithm. On PDEs with steep gradients, nonlinearity, and high dimensionality, the proposed method achieves consistently high accuracy, with relative L2 errors mostly in the range of O(10^-5) to O(10^-6), significantly surpassing baseline methods. Moreover, it offers greater reliability across diverse problems, whereas compared approaches often suffer from variable accuracy depending on the PDE. This work provides new insights into designing hybrid training strategies to enhance the performance and robustness of PINNs.