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.
Scientific machine learning through physics-informed neural networks: Where we are and what’s next.Journal of Scientific Computing, 92(3):88, 2022
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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.