Pages that link to "Item:Q5879394"
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The following pages link to Estimates on the generalization error of physics-informed neural networks for approximating PDEs (Q5879394):
Displaying 24 items.
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- Space-time error estimates for deep neural network approximations for differential equations (Q2683168) (← links)
- Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
- (Q5053337) (← links)
- A Rate of Convergence of Physics Informed Neural Networks for the Linear Second Order Elliptic PDEs (Q5077701) (← links)
- Some elliptic second order problems and neural network solutions: existence and error estimates (Q6073185) (← links)
- A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems (Q6076649) (← links)
- Deep neural network solution for finite state mean field game with error estimation (Q6078100) (← links)
- Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms (Q6087958) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- Higher-order error estimates for physics-informed neural networks approximating the primitive equations (Q6114171) (← links)
- Error analysis of deep Ritz methods for elliptic equations (Q6145797) (← links)
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
- A priori error estimate of deep mixed residual method for elliptic PDEs (Q6182315) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
- Deep learning of first-order nonlinear hyperbolic conservation law solvers (Q6560690) (← links)
- Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
- Inf-sup neural networks for high-dimensional elliptic PDE problems (Q6589859) (← links)
- Can neural networks learn finite elements? (Q6591550) (← links)
- Generalization of PINNs for elliptic interface problems (Q6595451) (← links)
- Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
- Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
- Error estimates of physics-informed neural networks for initial value problems (Q6647977) (← links)
- Improving weak PINNs for hyperbolic conservation laws: dual norm computation, boundary conditions and systems (Q6658817) (← links)
