RepNN reparameterizes the first hidden layer of DNNs to enable adaptive frequency scaling, improving accuracy on oscillatory and multiscale functions with minimal extra cost.
Enforcing hidden physics in physics- informed neural networks.arXiv preprint arXiv:2511.14348, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Empirical benchmarks indicate CViT-based PINOs with adapted PINN mitigations can match or exceed data-driven neural operators on parametric PDE tasks.
citing papers explorer
-
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.
-
On the training of physics-informed neural operators for solving parametric partial differential equations
Empirical benchmarks indicate CViT-based PINOs with adapted PINN mitigations can match or exceed data-driven neural operators on parametric PDE tasks.