A differentiable contact mechanics engine embedded in a neural network and quadratic optimizer discovers axisymmetric asperity topographies that produce target nonlinear friction laws, validated against BEM simulations.
Curriculum learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, Association for Computing Machinery, New York, NY, USA
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MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
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Inverse Design of Metainterfaces for Static Friction Control: Beyond the Hertzian Limit
A differentiable contact mechanics engine embedded in a neural network and quadratic optimizer discovers axisymmetric asperity topographies that produce target nonlinear friction laws, validated against BEM simulations.
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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.