On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
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DOI: 10.1007/s10444-024-10189-6
Abstract: Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tackling complex problems for which classical… read more here.
Keywords: reduced order; latent dimension; deep autoencoders; dimension deep ... See more keywords