GITO, a graph-informed transformer operator, reports lower relative L2 errors than existing transformer-based neural operators on Navier-Stokes, heat conduction, and airfoil benchmark datasets.
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification.Journal of Computational Physics, 366:415–447, 2018
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GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
GITO, a graph-informed transformer operator, reports lower relative L2 errors than existing transformer-based neural operators on Navier-Stokes, heat conduction, and airfoil benchmark datasets.