Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning

arXiv:2605.30375v1 Announce Type: cross Abstract: High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to large three-dimensional aircraft flows with multiscale regional heterogeneity. Most prior studies therefore focus on two-dimensional problems, surface qu

Source: arXiv cs.AI — read the full report at the original publisher.

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