Pages that link to "Item:Q6109143"
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The following pages link to Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143):
Displaying 12 items.
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
- Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation (Q2008102) (← links)
- Solution of physics-based Bayesian inverse problems with deep generative priors (Q2083099) (← links)
- Variational inference for nonlinear inverse problems via neural net kernels: comparison to Bayesian neural networks, application to topology optimization (Q2083125) (← links)
- Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy (Q2237307) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- Parameter inference with deep jointly informed neural networks (Q4970298) (← links)
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems (Q5162376) (← links)
- (Q5855574) (← links)
- Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance (Q6087365) (← links)
- Bayesian spatiotemporal modeling for inverse problems (Q6172144) (← links)
- Optimal neural network approximation of Wasserstein gradient direction via convex optimization (Q6633046) (← links)
