Chaotic maps act as augmentations in contrastive pre-training to learn topologically robust texture features, outperforming SOTA on six benchmarks when combined with attention-based fusion.
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2026 2verdicts
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PoreDiT generates 1024^3 voxel digital rock models via 3D Swin Transformer binary pore-field prediction, matching prior methods on porosity, permeability, and Euler characteristics while running on consumer hardware.
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Chaotic Contrastive Learning for Robust Texture Classification
Chaotic maps act as augmentations in contrastive pre-training to learn topologically robust texture features, outperforming SOTA on six benchmarks when combined with attention-based fusion.
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PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction
PoreDiT generates 1024^3 voxel digital rock models via 3D Swin Transformer binary pore-field prediction, matching prior methods on porosity, permeability, and Euler characteristics while running on consumer hardware.