Adding physics-guided loss terms to ConvLSTM, AFNONet, and ViViT improves fuel density prediction accuracy and stability over purely data-driven baselines on simulated prescribed-fire data.
Quic-fire: A fast-running simulation tool for prescribed fire planning,
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Physics-guided spatiotemporal neural models for fuel density prediction
Adding physics-guided loss terms to ConvLSTM, AFNONet, and ViViT improves fuel density prediction accuracy and stability over purely data-driven baselines on simulated prescribed-fire data.