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Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases.
An iterative multi-fidelity approach for model order reduction of multi-dimensional input parametric PDE systems
It is achieved by exploiting low-fidelity models throughout the parametric space to sample points using an efficient sampling strategy, and at the sampled parametric points, high-fidelity models are evaluated to recover the reduced basis functions.
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