Pages that link to "Item:Q4615657"
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The following pages link to Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ (Q4615657):
Displaying 26 items.
- Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\) (Q6109160) (← links)
- Convergence Rates for Learning Linear Operators from Noisy Data (Q6109175) (← links)
- Limitations of neural network training due to numerical instability of backpropagation (Q6122651) (← links)
- Collocation approximation by deep neural ReLU networks for parametric and stochastic PDEs with lognormal inputs (Q6148127) (← links)
- Optimal approximation of infinite-dimensional holomorphic functions (Q6151536) (← links)
- ReLU neural network Galerkin BEM (Q6159305) (← links)
- Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification (Q6164170) (← links)
- Neural network approximation and estimation of classifiers with classification boundary in a Barron class (Q6165247) (← links)
- Multilevel domain uncertainty quantification in computational electromagnetics (Q6175722) (← links)
- On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
- Energy-dissipative evolutionary deep operator neural networks (Q6187616) (← links)
- Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics (Q6196628) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
- Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
- Convergence rate of DeepONets for learning operators arising from advection-diffusion equations (Q6361196) (← links)
- Neural network expression rates and applications of the deep parametric PDE method in counterparty credit risk (Q6549602) (← links)
- Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
- Deep ReLU networks and high-order finite element methods. II: Chebyšev emulation (Q6585367) (← links)
- One-shot learning of surrogates in PDE-constrained optimization under uncertainty (Q6587616) (← links)
- Error analysis for deep neural network approximations of parametric hyperbolic conservation laws (Q6590625) (← links)
- Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
- Neural and spectral operator surrogates: unified construction and expression rate bounds (Q6601288) (← links)
- Parametric shape holomorphy of boundary integral operators with applications (Q6621317) (← links)
- On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields (Q6624464) (← links)
- Exploiting locality in sparse polynomial approximation of parametric elliptic PDEs and application to parameterized domains (Q6652068) (← links)
- Adaptive operator learning for infinite-dimensional Bayesian inverse problems (Q6669407) (← links)
