用傅里叶神经算子模拟对流，精度快于传统方法。
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
- 用傅里叶神经算子构建对流的代理模型
- 预测精度高，推理速度快，零样本超分辨率能力突出
- 适合流体控制等下游应用，尤其对复杂对流场景
我们训练了傅里叶神经算子（FNO）代理模型来模拟自然和工业中常见的瑞利-贝纳德对流（RBC）。以直接数值模拟（DNS）结果为真实标签，在不同条件下评估FNO与两种主流流体动力学代理模型——动态模态分解（DMD）和线性循环自编码器网络（LRAN）的预测精度与模型特性。结果显示，FNO在准确性与速度上均优于DMD和LRAN，且具备零样本超分辨率能力，可高效捕捉对流动态细节。该模型在后续任务如对流调控中具有广泛应用潜力。
原文摘要 · Abstract (English)
We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics: the Dynamic Mode Decomposition and the Linearly-Recurrent Autoencoder Network. We regard Direct Numerical Simulations (DNS) of the RBC equations as the ground truth on which the models are trained and evaluated in different settings. The FNO performs favorably when compared to the DMD and LRAN and its predictions are fast and highly accurate for this task. Additionally, we show its zero-shot super-resolution ability for the convection dynamics. The FNO model has a high potential to be used in downstream tasks such as flow control in RBC.
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。