Refined grid sampling speeds up neural modeling of scientific data
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data
Machine Learning
Summary
Training neural networks to represent complex physical data usually takes a lot of time because computers must check many points in space. The authors introduce a method called Refined Grid Sampling that smartly samples points without checking the entire grid each time. This new approach reduces wasted effort and helps the neural network learn more accurately and faster. They show their method works well on detailed fluid flow data, achieving similar accuracy much quicker than previous techniques.
What this means in practice
- •For computational fluid dynamics teams: Enable faster training of neural models that approximate fluid flow simulations for design and analysis tasks.
- •For climate modelers: Improve efficiency in creating compact neural representations of large-scale atmospheric data for faster experiments and storage.
Authors
Guang Zhao, Xihaier Luo, Sean McCue, Luke P. Van Roekel, Seungjun Lee, Yihui Ren, Shinjae Yoo, Wei Xu
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.