PD-SOVNet combines shared second-order vibration kernels, MIMO coupling, adaptive physical correction, and Mamba temporal modeling to regress 1st-40th order wheel roughness spectra from axle-box vibrations with competitive accuracy on real datasets.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.
citing papers explorer
-
PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations
PD-SOVNet combines shared second-order vibration kernels, MIMO coupling, adaptive physical correction, and Mamba temporal modeling to regress 1st-40th order wheel roughness spectra from axle-box vibrations with competitive accuracy on real datasets.
-
Self-supervised neural operator for solving partial differential equations
Self-supervised neural operator uses Bayesian PINNs to generate training data and a Transformer to learn PDE operators, achieving high accuracy on 1D/2D reaction-diffusion and fluid vibration problems with optional lightweight finetuning.
- Generative modeling of granular flow on inclined planes using conditional flow matching