Pages that link to "Item:Q2060092"
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The following pages link to Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092):
Displaying 21 items.
- Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
- Data-driven uncertainty quantification in computational human head models (Q2160383) (← links)
- Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335) (← links)
- Sparse polynomial approximations for affine parametric saddle point problems (Q2679763) (← links)
- Data assimilation -- mathematical foundation and applications. Abstracts from the workshop held February 20--26, 2022 (Q2693037) (← links)
- slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← 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)
- Convergence Rates for Learning Linear Operators from Noisy Data (Q6109175) (← links)
- Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering (Q6148532) (← links)
- Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs (Q6354925) (← links)
- A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
- Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
- PyOED: an extensible suite for data assimilation and model-constrained optimal design of experiments (Q6604163) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- Neural dynamical operator: continuous spatial-temporal model with gradient-based and derivative-free optimization methods (Q6648386) (← links)
- Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning (Q6652579) (← links)
