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Approximation bounds for convolutional neural networks in operator learning., , , and . CoRR, (2022)Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition., , , and . CoRR, (2023)Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy., , and . CoRR, (2024)On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields., , , and . CoRR, (2023)Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition., , , and . Adv. Comput. Math., 50 (3): 33 (June 2024)Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction., , , and . CoRR, (2024)Nonlinear model order reduction for problems with microstructure using mesh informed neural networks., , , , and . CoRR, (2023)A practical existence theorem for reduced order models based on convolutional autoencoders., and . CoRR, (2024)Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces., , and . J. Sci. Comput., 97 (2): 35 (November 2023)Learning Operators with Mesh-Informed Neural Networks., , and . CoRR, (2022)