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.
Engineering Applications of Artificial Intelligence163, 112587 (2026)
2 Pith papers cite this work. Polarity classification is still indexing.
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QHDE algorithm with dynamic quantum tunneling, chaos reverse learning, and elite perturbations outperforms seven prior methods by up to 96.6% on high-dimensional Sharpe ratio portfolio problems with 20-80 assets.
citing papers explorer
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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.
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A Quantum-Driven Evolutionary Framework for Solving High-Dimensional Sharpe Ratio Portfolio Optimization
QHDE algorithm with dynamic quantum tunneling, chaos reverse learning, and elite perturbations outperforms seven prior methods by up to 96.6% on high-dimensional Sharpe ratio portfolio problems with 20-80 assets.