Title: Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder Show Chinese title

Title: 通过多尺度扩散自编码器预测复杂系统的动力学

Abstract: Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the intrinsic dynamics. While existing methods capture key dynamics by encoding a low-dimensional latent space, they overlook the inherent multiscale structure of complex systems, making it difficult to accurately predict complex spatiotemporal evolution. Therefore, we propose a Multiscale Diffusion Prediction Network (MDPNet) that leverages the multiscale structure of complex systems to discover the latent space of intrinsic dynamics. First, we encode multiscale features through a multiscale diffusion autoencoder to guide the diffusion model for reliable reconstruction. Then, we introduce an attention-based graph neural ordinary differential equation to model the co-evolution across different scales. Extensive evaluations on representative systems demonstrate that the proposed method achieves an average prediction error reduction of 53.23% compared to baselines, while also exhibiting superior robustness and generalization. Show Chinese abstract

Abstract: 预测复杂系统的动力学对于各种科学和工程应用至关重要。 预测的准确性取决于模型捕捉内在动力学的能力。 虽然现有方法通过编码低维潜在空间来捕获关键动力学，但它们忽略了复杂系统固有的多尺度结构，使得准确预测复杂的时空演化变得困难。 因此，我们提出了一个多尺度扩散预测网络（MDPNet），利用复杂系统的多尺度结构来发现内在动力学的潜在空间。 首先，我们通过多尺度扩散自动编码器来编码多尺度特征，以指导可靠的重构扩散模型。 然后，我们引入基于注意力机制的图神经常微分方程来建模不同尺度之间的共同演化。 在代表性系统上的广泛评估表明，所提出的方法比基线平均预测误差减少了53.23%，同时表现出优越的鲁棒性和泛化能力。