arXiv:2509.15494v2 Announce Type: replace Abstract: Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (INRs) offer a promising solution by encoding signals through coordinate-based neural networks, serving as surrogates of data, with computational and storage requirements scaling with network complexity rather than data dimensionality. However, smaller INRs struggle to faithfully represent the multi-scale structures, high-frequency information, and fine textures that c
The continuous growth of scientific data necessitating more efficient and scalable representation methods makes innovations in implicit neural representations (INRs) critically important.
This development in INRs, particularly the enhancement for multi-resolution representation, promises to significantly improve data handling and analysis, impacting fields reliant on large, complex datasets.
The ability to encode high-frequency information and fine textures within INRs with smaller network sizes will reduce computational and storage burdens for scientific data.
- · AI/ML researchers
- · Scientific research institutions
- · Cloud computing providers
- · Data storage solution providers
- · Voxel-based data representation methods
- · Traditional high-performance computing methods for large datasets
Improved efficiency in processing and analyzing scientific data across various disciplines.
Acceleration of research and discovery in fields like physics, medicine, and climate science due to better data management.
New AI applications emerging from the ability to handle larger and more complex datasets more effectively, potentially reducing the 'compute supply chain' strain.
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Read at arXiv cs.LG