Pages that link to "Item:Q2679335"
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The following pages link to Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335):
Displaying 12 items.
- Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
- Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
- Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation (Q6159004) (← links)
- Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
- Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems (Q6185165) (← links)
- A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty (Q6185171) (← links)
- Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
- Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks (Q6385575) (← links)
- A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
- PyOED: an extensible suite for data assimilation and model-constrained optimal design of experiments (Q6604163) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
- Surrogate construction via weight parameterization of residual neural networks (Q6663269) (← links)
