用两阶段模型预测喷墨打印液滴长期演化，精度显著提升。
Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction
- 先用傅里叶MIONet做粗略预测，再用扩散模型修正细节
- 在ANSYS Fluent数据集上，长时序预测误差比现有方法低37%
- 适合需要高精度液滴演化模拟的工业打印场景
预测材料喷射或喷墨打印（IJP）中液滴的演化对保证打印质量至关重要。然而，由于误差累积和工艺变量间的复杂耦合，长期预测仍具挑战。本文提出扩散校正自回归傅里叶神经算子（DiffARFNO），一种两阶段框架：第一阶段使用自回归傅里叶-MIONet作为粗略预测器进行长期预报；第二阶段引入基于条件去噪扩散隐式模型（DDIM）的校正器，在每个滑动窗口内通过高效迭代去噪修正粗预测。结合傅里叶-MIONet的粗略预测与DDIM校正器的细粒度恢复，DiffARFNO旨在实现高保真度的长期预测。在来自ANSYS Fluent的液滴数据集上的大量实验表明，DiffARFNO显著优于现有最先进模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。