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arXiv AI By Pu Ren, Michael W. Mahoney, N. Benjamin Erichson

Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning

Read the original on arXiv AI →

The paper introduces iFLEX, a model that refines simple numerical interpolation for temporal gaps in scientific data. It first creates a weighted average of two observations, then trains a conditional diffusion model to generate the residual between this estimate and the true intermediate field. Experiments on turbulence, radar precipitation, and sea surface temperature show that iFLEX reduces reconstruction error compared to optical‑flow and other diffusion baselines.

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 AI.

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