通过分解损失函数提升神经网络对波浪的长期预测精度。
Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks
- 将损失函数拆分为相位和振幅两部分分别优化
- 显著减少长时间预测中的误差累积现象
- 适合需要高稳定性波浪模拟的工程场景
长时序准确预测对于建模复杂物理过程（如波传播）至关重要。尽管深度神经网络在实时预报中展现出潜力，但在长时间预测中常出现相位与振幅误差不断累积的问题。为此，我们提出一种新颖的损失分解策略，将损失函数分解为相位与振幅两个独立分量。该方法通过显式建模数值误差，提升了神经网络在波传播任务中的长期预测准确性，增强了模型稳定性，并有效抑制了长时间预报下的误差积累。
原文摘要 · Abstract (English)
Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time forecasting, they often struggle with accumulating phase and amplitude errors as predictions extend over a long period. To address this issue, we propose a novel loss decomposition strategy that breaks down the loss into separate phase and amplitude components. This technique improves the long-term prediction accuracy of neural networks in wave propagation tasks by explicitly accounting for numerical errors, improving stability, and reducing error accumulation over extended forecasts.
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