GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

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 https://github.com/trainsn/GNN-Surrogate.

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

“…Such emulators need to be able to sample physical parameters (not hyperparameter, but the parameter used in the physics parameterizations) within the parameter space. It should also work on both regular grids as well as non-regular mesh like the E3SM or MPAS-Ocean models do (Shi et al 2022) and should allow interactive post-hoc exploration and analysis, so the scientist can be analyzing the sensitivity of different parameters in real time during the training (He et al 2020).…”