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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 4roles
background 1polarities
background 1representative citing papers
The paper establishes rigorous theorems proving the Frequency Principle holds for general deep neural networks at initial, intermediate, and final training stages.
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.
citing papers explorer
-
KAN: Kolmogorov-Arnold Networks
KANs with learnable univariate spline activations on edges achieve better accuracy than MLPs with fewer parameters, faster scaling, and direct visualization for scientific discovery.
-
Theory of the Frequency Principle for General Deep Neural Networks
The paper establishes rigorous theorems proving the Frequency Principle holds for general deep neural networks at initial, intermediate, and final training stages.
-
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.
-
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.