Researchers have introduced a novel hybrid least squares/gradient descent (LSGD) method designed to accelerate the training of MIONets. This approach extends existing LSGD techniques used for DeepONets. The method treats MIONets as multilinear functions, enabling optimization of parameters through an alternating least squares process for individual branch networks. To manage large system matrices, the technique leverages Kronecker and Khatri-Rao products along with tensor permutation matrices for efficient factorization. AI
IMPACT This method could lead to faster training times for MIONets, potentially enabling more complex model architectures and applications.
RANK_REASON The cluster describes a new method proposed in a research paper published on arXiv.
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- arXiv
- cs.AI
- cs.LG
- DeepONets
- MIONets
- gradient descent
- least squares method
- tensor permutation matrices
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