Pages that link to "Item:Q4997381"
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The following pages link to Construct Deep Neural Networks based on Direct Sampling Methods for Solving Electrical Impedance Tomography (Q4997381):
Displaying 16 items.
- The use of neural network approximation models to speed up the optimisation process in electrical impedance tomography (Q839172) (← links)
- Learning nonlinear electrical impedance tomography (Q2063189) (← links)
- Neural networks for classification of strokes in electrical impedance tomography on a 3D head model (Q2167606) (← links)
- A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (Q2179220) (← links)
- Solving electrical impedance tomography with deep learning (Q2223016) (← links)
- A direct sampling method for electrical impedance tomography (Q2924859) (← links)
- Neural networks for FDTD‐backed permittivity reconstruction (Q4672416) (← links)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
- Electrical impedance tomography with deep Calderón method (Q6048408) (← links)
- Divide-and-conquer DNN approach for the inverse point source problem using a few single frequency measurements (Q6058333) (← links)
- Learn an index operator by CNN for solving diffusive optical tomography: a deep direct sampling method (Q6101530) (← links)
- A direct sampling-based deep learning approach for inverse medium scattering problems (Q6141551) (← links)
- A Data-Assisted Two-Stage Method for the Inverse Random Source Problem (Q6144051) (← links)
- Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems (Q6557671) (← links)
- Solving inverse obstacle scattering problem with latent surface representations (Q6557689) (← links)
- A comparative study of variational autoencoders, normalizing flows, and score-based diffusion models for electrical impedance tomography (Q6583089) (← links)
