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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AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
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).