Pages that link to "Item:Q2125441"
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The following pages link to Physics-informed semantic inpainting: application to geostatistical modeling (Q2125441):
Displaying 10 items.
- Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855) (← links)
- INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
- The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning (Q2113140) (← links)
- A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
- Learning functional priors and posteriors from data and physics (Q2135824) (← links)
- Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
- Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
- A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics (Q6109265) (← links)
- The line rogue wave solutions of the nonlocal Davey-Stewartson I equation with \textit{PT} symmetry based on the improved physics-informed neural network (Q6571797) (← links)
- Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264) (← links)
