E-NES uses Lie-group point-cloud conditioning and equivariant neural fields to make grid-free eikonal travel-time prediction steerable under rotations and translations, with complete invariant features and competitive OpenFWI benchmark results.
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Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
E-NES uses Lie-group point-cloud conditioning and equivariant neural fields to make grid-free eikonal travel-time prediction steerable under rotations and translations, with complete invariant features and competitive OpenFWI benchmark results.