发表机构
New Jersey Institute of Technology; University of Georgia; University at Buffalo; Hanyang University; Northeastern University（新泽西理工学院; 佐治亚大学; 纽约州立大学布法罗分校; 汉阳大学; 东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究材料喷射中液滴演化预测难题，提出DiffARFNO两阶段框架，结合自回归傅里叶 - MIONet与DDIM校正器，经实验验证该方法显著优于现有模型，能为长期预测提供高保真结果。
AI 中文摘要
预测材料喷射（即喷墨打印，IJP）中的液滴演化对于维持打印质量至关重要。然而，由于误差累积和过程变量的复杂耦合，长期预测仍然具有挑战性。在这项工作中，我们引入了扩散校正自回归傅里叶神经算子（DiffARFNO），这是一个两阶段框架，它将自回归傅里叶 - MIONet与条件去噪扩散隐式模型（DDIM）校正器相结合。傅里叶 - MIONet被训练为粗预测器并自回归地用于长期预测。在第二阶段，基于DDIM的条件校正器通过有效的迭代去噪在每个滑动窗口内细化粗预测。通过将傅里叶 - MIONet的粗预测与恢复精细细节的DDIM校正器相结合，DiffARFNO旨在为长期预测提供高保真预测。在ANSYS Fluent的液滴数据集上进行的大量实验表明，DiffARFNO明显优于现有的最先进模型。
英文摘要
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.
Comments11 figures, 4 tables
