Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning
Computationally expensive simulations and real-world observations often provide data at a lower temporal resolution than downstream tasks require. Averaging observations at fixed grid points can introduce errors, and motion estimates alone do not determine how the physical field values change. In this work, we study whether learning to correct a simple numerical interpolation improves the reconstruction of intermediate scientific fields. Our model, interpolative FLow EXpert (iFLEX), first estimates the missing field by taking a weighted average of the two observations at each grid point, giving more weight to the observation closer to the requested time. During training, subtracting this ...
| Authors | Pu Ren, Michael W. Mahoney, N. Benjamin Erichson |
|---|---|
| Primary category | cs.LG |
| Published | 27 October 2026 |
| Last updated at source | 27 October 2026 |
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