arXiv Machine Learning June 5, 2026 By Yifan Chen, Eric Vanden-Eijnden

Scale-Adaptive Generative Flows for Multiscale Scientific Data

Read the original on arXiv Machine Learning →

arXiv:2509. 02971v2 Announce Type: replace-cross Abstract: Flow-based generative models can face numerical challenges on scientific data with multiscale Fourier spectra, often producing large errors at fine scales.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

diffusion

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Jun 2

Low-Pass Flow Matching

arXiv:2606. 02177v1 Announce Type: new Abstract: Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with frequency.

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diffusion safety

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diffusion

arXiv Machine Learning

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A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations

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diffusion efficiency

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arXiv Machine Learning

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diffusion efficiency multimodal

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