Pages that link to "Item:Q2083099"
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The following pages link to Solution of physics-based Bayesian inverse problems with deep generative priors (Q2083099):
Displaying 15 items.
- Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
- Learning functional priors and posteriors from data and physics (Q2135824) (← links)
- Solving inverse problems in stochastic models using deep neural networks and adversarial training (Q2237477) (← links)
- Solving Bayesian inverse problems from the perspective of deep generative networks (Q2319396) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- A Generative Variational Model for Inverse Problems in Imaging (Q5065477) (← links)
- Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior (Q5094620) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty (Q6147033) (← links)
- A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows (Q6194145) (← links)
- Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems (Q6194148) (← links)
- Bayesian imaging inverse problem with SA-roundtrip prior via HMC-pCN sampler (Q6573297) (← links)
- Data-driven variational method for discrepancy modeling: dynamics with small-strain nonlinear elasticity and viscoelasticity (Q6648552) (← links)
- Conditional score-based diffusion models for solving inverse elasticity problems (Q6663241) (← links)
