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Efficient and accurate training of physics-informed deep operator networks with the conjugate kernel
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Recent work has shown that the empirical Neural Tangent Kernel (NTK) can significantly improve the training of physics-informed Deep Operator Network (DeepONets). The NTK, however, is costly to calculate, greatly increasing the cost to train such systems. We study the performance of the empirical Conjugate Kernel (CK) for physics-informed DeepONets, an efficient approximation to the NTK that has been observed to yield similar results. For physics-informed DeepONets, we show that the CK performance is comparable to the NTK, while significantly reducing time complexity for training DeepONets.
