Showing 1–16 of 16 results
/ Date/ Name
Aug 3, 2023Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networksOct 18, 2023On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fieldsMar 22, 2022Mesh-Informed Neural Networks for Operator Learning in Finite Element SpacesSep 13, 2024Measurability and continuity of parametric low-rank approximation in Hilbert spaces: linear operators and random variablesJul 4, 2022Approximation bounds for convolutional neural networks in operator learningApr 29, 2024Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reductionNov 18, 2025Nonparametric estimation of conditional probability distributions using a generative approach based on conditional push-forward neural networksFeb 1, 2024A practical existence theorem for reduced order models based on convolutional autoencodersMay 8, 2023Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decompositionFeb 13, 2024Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapyMay 13, 2025Numerical Solution of Mixed-Dimensional PDEs Using a Neural PreconditionerFeb 28, 2026A short tour of operator learning theory: Convergence rates, statistical limits, and open questionsSep 14, 2023Nonlinear model order reduction for problems with microstructure using mesh informed neural networksJun 13, 2025Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveMar 10, 2021A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential EquationsFeb 23, 2021Learning High-Order Interactions via Targeted Pattern Search