We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input parameters (e.g., wind stress) on the simulation output (e.g., temperature). The exploration requires scientists to exhaust the complicated parameter space by running a batch of computationally expensive simulations. Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently. Specifically, GNN-Surrogate predicts the output field with given simulation parameters so scientists can explore the simulation parameter space with visualizations from user-specified visual mappings. Moreover, our graph-based techniques are designed for unstructured meshes, making the exploration of simulation outputs on irregular grids efficient. For efficient training, we generate hierarchical graphs and use adaptive resolutions. We give quantitative and qualitative evaluations on the MPAS-Ocean simulation to demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is publicly available at https://github.com/trainsn/GNN-Surrogate.
翻译:我们提议GNN-Surrogate(GNN-Surrogate)为探索海洋气候模拟的参数空间。参数空间探索对于域科学家了解输入参数(例如风应力)对模拟输出(例如温度)的影响非常重要。探索要求科学家通过运行一组计算成本昂贵的模拟来用尽复杂的参数空间。我们的方法通过一个精确和高效地预测模拟输出结果的替代模型来提高参数空间探索的效率。具体地说,GNN-Surrogate(GNN-Surrogate)以一定的模拟参数预测输出场,以便科学家能够从用户指定的视觉绘图中以可视化的方式探索模拟参数空间。此外,我们的基于图形的技术是为非结构化的模层设计,从而高效地探索不规则电网上的模拟输出。为了高效的培训,我们制作了等级图,并使用适应性分辨率。我们对磁数据-海洋模拟进行定量和定性评估,以显示GNNN-Surrogate的效能和效率。源代码可在https://gthub.comtrains/trainsn-GNNNNSgate上公开查阅。