Pages that link to "Item:Q2095545"

The following pages link to Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545):

Displaying 20 items.

• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
• Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms (Q6087958) (← links)
• Reliable extrapolation of deep neural operators informed by physics or sparse observations (Q6097626) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← 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)
• A theoretical case study of the generalization of machine-learned potentials (Q6125490) (← links)
• Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
• Meshless methods for American option pricing through physics-informed neural networks (Q6158655) (← links)
• Mobility Estimation for Langevin Dynamics Using Control Variates (Q6178098) (← links)
• Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)
• Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
• Decoupling numerical method based on deep neural network for nonlinear degenerate interface problems (Q6592742) (← 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)
• Binary structured physics-informed neural networks for solving equations with rapidly changing solutions (Q6615737) (← links)
• A short note on solving partial differential equations using convolutional neural networks (Q6620254) (← links)
• Deep adaptive sampling for surrogate modeling without labeled data (Q6639518) (← links)
• Error analysis of kernel/GP methods for nonlinear and parametric PDEs (Q6648398) (← links)