标题： DiffPINN：用于加速地震波场表示的生成扩散初始化物理信息神经网络 显示英文标题

标题： DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation

摘要： 物理信息神经网络（PINNs）为地震波场建模提供了一个强大的框架，但它们通常需要耗费大量时间重新训练才能应用于不同的速度模型。 此外，由于波场解的复杂性，它们的训练可能会因收敛缓慢而受到影响。 为了解决这些挑战，我们引入了一种基于潜在扩散的策略，用于快速有效的PINNs初始化。 首先，我们训练多个PINNs来表示不同速度模型的频域散射波场，然后将每个训练好的网络参数展平为一维向量，创建一个全面的参数数据集。 接下来，我们使用自动编码器学习这些参数向量的潜在表示，捕捉不同PINNs参数中的关键模式。 然后，我们训练一个条件扩散模型来存储这些潜在向量的分布，相应的速度模型作为条件。 一旦训练完成，这个扩散模型可以生成对应于新速度模型的潜在向量，随后由自动编码器解码为完整的PINNs参数。 实验结果表明，我们的方法显著加速了训练，并在分布内和分布外的速度场景中保持了高精度。 显示英文摘要

摘要： Physics-informed neural networks (PINNs) offer a powerful framework for seismic wavefield modeling, yet they typically require time-consuming retraining when applied to different velocity models. Moreover, their training can suffer from slow convergence due to the complexity of of the wavefield solution. To address these challenges, we introduce a latent diffusion-based strategy for rapid and effective PINN initialization. First, we train multiple PINNs to represent frequency-domain scattered wavefields for various velocity models, then flatten each trained network's parameters into a one-dimensional vector, creating a comprehensive parameter dataset. Next, we employ an autoencoder to learn latent representations of these parameter vectors, capturing essential patterns across diverse PINN's parameters. We then train a conditional diffusion model to store the distribution of these latent vectors, with the corresponding velocity models serving as conditions. Once trained, this diffusion model can generate latent vectors corresponding to new velocity models, which are subsequently decoded by the autoencoder into complete PINN parameters. Experimental results indicate that our method significantly accelerates training and maintains high accuracy across in-distribution and out-of-distribution velocity scenarios.