<ul><li>Physics-based deep learning frameworks are effective in modeling complex physical systems with generalization capability.</li><li>A novel GNN architecture, Multi-Fidelity U-Net, utilizes multi-fidelity methods to enhance GNN model performance.</li><li>The proposed approach reduces data requirements and performs better in accuracy compared to benchmark multi-fidelity approaches.</li><li>The proposed models provide a feasible alternative for addressing computational and accuracy requirements in time-consuming simulations.</li></ul>

A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations

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