RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
A Causality-DeepONet for Causal Responses of Linear Dynamical Systems
3 Pith papers cite this work, alongside 10 external citations. Polarity classification is still indexing.
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representative citing papers
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
ModalONet recovers the modal basis of beams and plates directly from noisy response fields using a DeepONet-style factorization with Laplace poles, reaching MAC ≥ 0.998 on synthetic benchmarks.
citing papers explorer
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RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization
RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
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Learning Structural Eigenmodes with Modal Operator Network (ModalONet)
ModalONet recovers the modal basis of beams and plates directly from noisy response fields using a DeepONet-style factorization with Laplace poles, reaching MAC ≥ 0.998 on synthetic benchmarks.