论文标题
用图网络模拟液体
Simulating Liquids with Graph Networks
论文作者
论文摘要
使用传统的模拟器模拟复杂的动态（例如流体）在计算上具有挑战性。深度学习模型已被认为是一种有效的替代方案，扩展或替换了传统模拟器的一部分。我们研究了用于学习流体动力学的图形神经网络（GNN），发现它们的概括能力比以前的作品所暗示的要有限。我们还挑战了当前在网络输入中添加随机噪声的实践，以提高其概括能力和仿真稳定性。我们发现插入真实的数据分布，例如通过展开多个仿真步骤，提高准确性并隐藏所有特定领域的特征从学习模型中提高了概括。我们的结果表明，除非训练集没有任何可以用作快捷方式的其他特定于问题的相关性，否则学习模型（例如GNNS）无法学习确切的潜在动态。
Simulating complex dynamics like fluids with traditional simulators is computationally challenging. Deep learning models have been proposed as an efficient alternative, extending or replacing parts of traditional simulators. We investigate graph neural networks (GNNs) for learning fluid dynamics and find that their generalization capability is more limited than previous works would suggest. We also challenge the current practice of adding random noise to the network inputs in order to improve its generalization capability and simulation stability. We find that inserting the real data distribution, e.g. by unrolling multiple simulation steps, improves accuracy and that hiding all domain-specific features from the learning model improves generalization. Our results indicate that learning models, such as GNNs, fail to learn the exact underlying dynamics unless the training set is devoid of any other problem-specific correlations that could be used as shortcuts.
