Augmenting neural field inputs with a clamped distance function lets a single network represent gradient discontinuities at material interfaces and creases, enabling discretization-agnostic reduced-order physics simulation.
InProceedings of Graphics Interface 2022(Montréal, Quebec)(GI 2022)
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Precise Gradient Discontinuities in Neural Fields for Subspace Physics
Augmenting neural field inputs with a clamped distance function lets a single network represent gradient discontinuities at material interfaces and creases, enabling discretization-agnostic reduced-order physics simulation.