KANs with learnable univariate spline activations on edges achieve better accuracy than MLPs with fewer parameters, faster scaling, and direct visualization for scientific discovery.
Relu deep neural networks and linear finite elements
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A deep learning framework forecasts final wildfire burned area extent from ignition-time data, with an ablation showing that a four-day pre- to five-day post-ignition temporal window improves F1 and IoU by nearly 5% over a single-day baseline on held-out Mediterranean test data.
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
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Wildfire spread forecasting with Deep Learning
A deep learning framework forecasts final wildfire burned area extent from ignition-time data, with an ablation showing that a four-day pre- to five-day post-ignition temporal window improves F1 and IoU by nearly 5% over a single-day baseline on held-out Mediterranean test data.