@article{shi22_gnn,
    author = {Shi, Neng and Xu, Jiayi and Wurster, Skylar W. and Guo, Hanqi and Woodring, Jonathan and Van Roekel, Luke P. and Shen, Han-Wei},
    title = {GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations},
    year = {2022},
    issue_date = {June 2022},
    publisher = {IEEE Educational Activities Department},
    address = {USA},
    volume = {28},
    number = {6},
    issn = {1077-2626},
    url = {https://doi.org/10.1109/TVCG.2022.3165345},
    doi = {10.1109/TVCG.2022.3165345},
    abstract = {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 <uri>https://github.com/trainsn/GNN-Surrogate</uri>.},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    month = {jun},
    pages = {2301–2313},
    numpages = {13}
}