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.
Training physics-informed neural networks: One learning to rule them all?Results in Engineering, 18:101023
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A spatially correlated curriculum learning framework for PINNs using causal weights, low-frequency bridges, and adaptive reweighting to reduce training failures on spatially coupled BVPs.
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Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation
A spatially correlated curriculum learning framework for PINNs using causal weights, low-frequency bridges, and adaptive reweighting to reduce training failures on spatially coupled BVPs.