Pages that link to "Item:Q5075685"
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The following pages link to Error estimates for DeepONets: a deep learning framework in infinite dimensions (Q5075685):
Displaying 50 items.
- Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
- Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
- Improved architectures and training algorithms for deep operator networks (Q2149522) (← links)
- Variational physics informed neural networks: the role of quadratures and test functions (Q2162334) (← links)
- RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
- Neural networks in Fréchet spaces (Q2679424) (← links)
- SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity (Q5051381) (← links)
- (Q5053337) (← links)
- Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
- A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
- Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks (Q5066584) (← links)
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
- Reliable extrapolation of deep neural operators informed by physics or sparse observations (Q6097626) (← links)
- Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations (Q6108133) (← links)
- Convergence Rates for Learning Linear Operators from Noisy Data (Q6109175) (← links)
- The Kolmogorov infinite dimensional equation in a Hilbert space via deep learning methods (Q6112485) (← links)
- A multifidelity deep operator network approach to closure for multiscale systems (Q6116145) (← links)
- Connections between Operator-Splitting Methods and Deep Neural Networks with Applications in Image Segmentation (Q6151361) (← links)
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
- Variationally mimetic operator networks (Q6185143) (← links)
- En-DeepONet: an enrichment approach for enhancing the expressivity of neural operators with applications to seismology (Q6194144) (← links)
- Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics (Q6196628) (← links)
- Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
- Approximation bounds for convolutional neural networks in operator learning (Q6403941) (← links)
- Basis operator network: a neural network-based model for learning nonlinear operators via neural basis (Q6488825) (← links)
- Approximation of smooth functionals using deep ReLU networks (Q6488836) (← links)
- Numerical solutions of boundary problems in partial differential equations: a deep learning framework with Green's function (Q6560698) (← links)
- Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
- The Calderón's problem via DeepONets (Q6570540) (← links)
- Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation (Q6572185) (← links)
- On the training and generalization of deep operator networks (Q6573171) (← links)
- Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Q6581233) (← links)
- Learning homogenization for elliptic operators (Q6583661) (← links)
- Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
- Solving parametric elliptic interface problems via interfaced operator network (Q6589882) (← 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)
- Pseudo-differential integral autoencoder network for inverse PDE operators (Q6601306) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- Mini-workshop: Nonlinear approximation of high-dimensional functions in scientific computing. Abstracts from the mini-workshop held October 15--20, 2023 (Q6613392) (← links)
- On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields (Q6624464) (← links)
- Approximation and generalization of DeepONets for learning operators arising from a class of singularly perturbed problems (Q6630935) (← links)
- A discretization-invariant extension and analysis of some deep operator networks (Q6633297) (← links)
- Learning the Hodgkin-Huxley model with operator learning techniques (Q6641924) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
