Pages that link to "Item:Q2055067"
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The following pages link to Approximation rates for neural networks with encodable weights in smoothness spaces (Q2055067):
Displaying 30 items.
- Approximation rates for neural networks with general activation functions (Q1982446) (← links)
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
- Interpolation and approximation via momentum ResNets and neural ODEs (Q2124500) (← links)
- Uniform approximation rates and metric entropy of shallow neural networks (Q2157931) (← links)
- A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
- Imaging conductivity from current density magnitude using neural networks* (Q5081798) (← links)
- Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
- Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
- Construction and approximation for a class of feedforward neural networks with sigmoidal function (Q6052306) (← links)
- Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
- On the approximation of functions by tanh neural networks (Q6055124) (← links)
- Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
- Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
- Approximation error for neural network operators by an averaged modulus of smoothness (Q6093307) (← links)
- Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method (Q6095431) (← links)
- Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning (Q6108164) (← links)
- A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs (Q6143000) (← links)
- Error analysis of deep Ritz methods for elliptic equations (Q6145797) (← links)
- Convergence Analysis of a Quasi-Monte CarloBased Deep Learning Algorithm for Solving Partial Differential Equations (Q6151262) (← links)
- Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions (Q6151354) (← links)
- Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
- Deep learning based on randomized quasi-Monte Carlo method for solving linear Kolmogorov partial differential equation (Q6582041) (← links)
- Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
- Recovering the source term in elliptic equation via deep learning: method and convergence analysis (Q6586293) (← links)
- Error analysis for deep neural network approximations of parametric hyperbolic conservation laws (Q6590625) (← links)
- Current density impedance imaging with PINNs (Q6591509) (← links)
- Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
- Convergence analysis for over-parameterized deep learning (Q6608346) (← links)
- Asymptotic analysis of neural network operators employing the Hardy-Littlewood maximal inequality (Q6649179) (← links)
- Error analysis of the mixed residual method for elliptic equations (Q6662404) (← links)
