Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks

Indu Kant Deo; Rajeev Jaiman

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arXiv · 2412.02924

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks

Indu Kant Deo·Rajeev Jaiman

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Abstract

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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Indu Kant Deo, Rajeev Jaiman. 2024-12-04. Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks. https://arxiv.org/abs/2412.02924

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