Picture for Niklas Linde

Niklas Linde

Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks

Jul 29, 2023
Figure 1 for Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks
Figure 2 for Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks
Figure 3 for Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks
Figure 4 for Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks
Viaarxiv icon

Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors

Sep 08, 2021
Figure 1 for Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors
Figure 2 for Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors
Figure 3 for Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors
Figure 4 for Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors
Viaarxiv icon

Fast ABC with joint generative modelling and subset simulation

Apr 16, 2021
Figure 1 for Fast ABC with joint generative modelling and subset simulation
Figure 2 for Fast ABC with joint generative modelling and subset simulation
Figure 3 for Fast ABC with joint generative modelling and subset simulation
Figure 4 for Fast ABC with joint generative modelling and subset simulation
Viaarxiv icon

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Add code
Oct 25, 2017
Figure 1 for Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Figure 2 for Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Figure 3 for Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Figure 4 for Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Viaarxiv icon

Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network

Add code
Aug 16, 2017
Figure 1 for Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network
Figure 2 for Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network
Figure 3 for Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network
Figure 4 for Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network
Viaarxiv icon