{"slug": "diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution", "title": "Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction", "summary": "Researchers introduce the Diffusion-corrected Auto-Regressive Fourier Neural Operator (DiffARFNO), a two-stage framework combining an autoregressive Fourier-MIONet with a conditional Denoising Diffusion Implicit Model (DDIM) corrector, to predict droplet evolution in material jetting. Experiments on droplet datasets from ANSYS Fluent show DiffARFNO significantly outperforms existing state-of-the-art models for long-horizon forecasts.", "body_md": "arXiv:2607.16238v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution", "canonical_source": "https://arxiv.org/abs/2607.16238", "published_at": "2026-07-21 04:00:00+00:00", "updated_at": "2026-07-21 04:12:30.212332+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence"], "entities": ["DiffARFNO", "Fourier-MIONet", "DDIM", "ANSYS Fluent"], "alternates": {"html": "https://wpnews.pro/news/diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution", "markdown": "https://wpnews.pro/news/diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution.md", "text": "https://wpnews.pro/news/diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution.txt", "jsonld": "https://wpnews.pro/news/diffusion-corrected-autoregressive-fourier-neural-operator-for-droplet-evolution.jsonld"}}