Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data

Abstract:Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient because evaluating dense coordinate grids is computationally expensive. Existing coordinate-pruning strategies rely on local error functions that can oversample correlated points and incur substantial overhead from full-grid error evaluation. We introduce Refined Grid Sampling (RGS), which combines stratified grid sampling, error-based refinement, and dynamic grid-resolution adjustment. This design reduces spatial redundancy without evaluating errors over the full grid. On high-resolution Navier--Stokes fields, RGS achieves higher reconstruction accuracy within a fixed wall-clock budget and reaches the accuracy of full-grid training up to $6\times$ faster than the evaluated baselines.

Machine Learning (cs.LG)

arXiv:2610.14205 [cs.LG]

(or arXiv:2610.14205v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14205 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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