Pages that link to "Item:Q5052353"
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The following pages link to HESSIAN-BASED SAMPLING FOR HIGH-DIMENSIONAL MODEL REDUCTION (Q5052353):
Displaying 14 items.
- Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092) (← links)
- Optimal design of acoustic metamaterial cloaks under uncertainty (Q2128381) (← links)
- Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (Q2214671) (← links)
- Bayesian inference of heterogeneous epidemic models: application to COVID-19 spread accounting for long-term care facilities (Q2237746) (← links)
- A training set subsampling strategy for the reduced basis method (Q2666028) (← links)
- Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors (Q5146679) (← links)
- Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (Q5158925) (← links)
- Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference (Q5880609) (← links)
- An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement (Q5886849) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (Q6109162) (← links)
- A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models (Q6146995) (← links)
- Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
- A local approach to parameter space reduction for regression and classification tasks (Q6536826) (← links)
