We introduce a new SGD-based algorithm with delayed projection for training kernel machines that achieves comparable or superior performance while reducing training time from days to under an hour.

We introduce Woodbury's matrix identity, momentum-like SPRING and randomization to make energy natural gradient descent 75 times faster for PINNs.