Proposes a model-free representing-then-learning framework with zeroth-order derivative estimators from perturbed Monte Carlo trajectories to solve fully nonlinear parabolic PDEs with unknown coefficients in high dimensions.
arXiv preprint arXiv:2104.05512 , year=
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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.
The paper reviews data sources, physical models, downstream applications, and AI techniques to outline considerations for building a foundation model for the Martian atmosphere.
citing papers explorer
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A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients
Proposes a model-free representing-then-learning framework with zeroth-order derivative estimators from perturbed Monte Carlo trajectories to solve fully nonlinear parabolic PDEs with unknown coefficients in high dimensions.
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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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Towards a Foundation Model for the Martian Atmosphere
The paper reviews data sources, physical models, downstream applications, and AI techniques to outline considerations for building a foundation model for the Martian atmosphere.
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