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Generative inpainting of incomplete Euclidean distance matrices of trajectories generated by a fractional Brownian motion
Abstract Fractional Brownian motion (fBm) exhibits both randomness and strong scale-free correlations, posing a challenge for generative artificial intelligence to replicate the underlying stochastic process. In this study, we evaluate the performance of diffusion-based inpainting methods on a speci...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Portfolio
2025-05-01
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| Serie: | Scientific Reports |
| Accesso online: | https://doi.org/10.1038/s41598-025-97893-5 |
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