Introduces C-Adam optimizer variant with claimed convergence proof and real-life numerical experiments.
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate
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A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
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A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
Introduces C-Adam optimizer variant with claimed convergence proof and real-life numerical experiments.
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Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.