Researchers have developed a novel hybrid least squares/gradient descent (LSGD) method to accelerate the training of MIONets. This technique extends existing LSGD methods for DeepONets by treating MIONets as multilinear functions. The approach optimizes parameters for each branch network iteratively using alternating least squares, employing Kronecker and Khatri-Rao products to manage large system matrices. This method supports various $L^2$ loss functions with regularization for the last layer parameters of branch networks. AI
IMPACT Introduces a more efficient training method for a specific class of neural networks, potentially speeding up research and development in areas utilizing MIONets.
RANK_REASON This is a research paper detailing a new method for training neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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