CGMPINN combines Gaussian mixture modeling with curriculum learning to reduce training errors in physics-informed neural networks by up to 97.8% on benchmark PDEs while providing theoretical convergence guarantees.
Physics-informed neural networks for cardiac activation mapping.Fron- tiers in Physics, 8:42, 2020
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From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
CGMPINN combines Gaussian mixture modeling with curriculum learning to reduce training errors in physics-informed neural networks by up to 97.8% on benchmark PDEs while providing theoretical convergence guarantees.