arXiv:2606.29723v1 Announce Type: new Abstract: Continuous physical fields represent a large fraction of data under scientific investigation. Their multiscale structures are central to discovery, yet useful coordinates are not known in advance. Standard self-supervised methods define context and targets in fixed image coordinates, posing a predictive task misaligned with fields organized across a continuous scale hierarchy. We introduce ScaleAware-JEPA, a framework that constructs dense, label-free latent coordinates for continuous scalar fields. Constrained Diffusion Decomposition (CDD) separ
The continuous advancement in AI, particularly in self-supervised learning and generative models, is enabling new approaches for understanding complex scientific data.
ScaleAware-JEPA offers a method to create label-free latent coordinates for continuous scalar fields, which can accelerate discovery in vast scientific datasets where explicit annotations are infeasible.
This framework could transform how researchers analyze and extract insights from multiscale physical fields across various scientific disciplines, particularly in areas like astrophysics, materials science, and climate modeling.
- · Scientific research institutions
- · Astrophysics
- · Materials science
- · AI/ML researchers
- · Traditional manual data annotation methods
- · Researchers dependent solely on supervised learning for complex physical data
Improved efficiency and accuracy in scientific discovery through automated latent representation generation.
Faster development cycles for new materials, drugs, or predictive models in fields leveraging multiscale physical data.
Potential for new scientific breakthroughs previously unattainable due to the complexity and scale of continuous field analysis.
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Read at arXiv cs.LG