Pages that link to "Item:Q6068232"
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The following pages link to Three ways to solve partial differential equations with neural networks — A review (Q6068232):
Displaying 11 items.
- On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
- A decoupled physics-informed neural network for recovering a space-dependent force function in the wave equation from integral overdetermination data (Q6103366) (← links)
- An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis (Q6159333) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
- Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
- Semi-analytic PINN methods for boundary layer problems in a rectangular domain (Q6581956) (← links)
- Cubic and quartic hyperbolic B-splines comparison for coupled Navier Stokes equation via differential quadrature method -- a statistical aspect (Q6590251) (← links)
- Deep neural networks for probability of default modelling (Q6593214) (← links)
- Solving American option optimal control problems in financial markets using a novel neural network (Q6593226) (← links)
- Time integration schemes based on neural networks for solving partial differential equations on coarse grids (Q6670111) (← links)
