Switching between coarse and fine collocation datasets during PINN training, like a multigrid V-cycle, improves accuracy by up to 64 percent on lid-driven cavity benchmarks.
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Multi-level datasets training method in Physics-Informed Neural Networks
Switching between coarse and fine collocation datasets during PINN training, like a multigrid V-cycle, improves accuracy by up to 64 percent on lid-driven cavity benchmarks.