Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction
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Computer Science > Machine Learning
Title:Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction
Abstract:Predicting droplet evolution in material jetting, or Inkjet Printing (IJP), is essential for maintaining printing quality. However, long-horizon forecasts remain challenging due to error accumulation and the complex coupling of process variables. In this work, we introduce the Diffusion-corrected Auto-Regressive Fourier Neural Operator (DiffARFNO), a two-stage framework that combines an autoregressive Fourier-MIONet with a conditional Denoising Diffusion Implicit Model (DDIM) corrector. Fourier-MIONet is trained as a coarse predictor and deployed autoregressively for long-horizon forecasting. In the second stage, a DDIM-based conditional corrector refines the coarse prediction within each sliding window through efficient iterative denoising. By combining coarse predictions from Fourier-MIONet with a DDIM corrector that restores fine details, DiffARFNO aims to provide high-fidelity predictions for long-horizon forecasts. Extensive experiments on droplet datasets from ANSYS Fluent demonstrate that DiffARFNO significantly outperforms existing state-of-the-art models.
| Comments: | 11 figures, 4 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2607.16238 [cs.LG] |
| (or arXiv:2607.16238v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16238
arXiv-issued DOI via DataCite
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