The following pages link to (Q5053337):
Displaying 42 items.
- Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator (Q2679950) (← links)
- Local approximation of operators (Q2689140) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
- On Bayesian data assimilation for PDEs with ill-posed forward problems (Q5089412) (← 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)
- DNN modeling of partial differential equations with incomplete data (Q6094765) (← 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)
- Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems (Q6153910) (← links)
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
- An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis (Q6159333) (← links)
- Variationally mimetic operator networks (Q6185143) (← links)
- Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
- Designing universal causal deep learning models: The geometric (Hyper)transformer (Q6196301) (← links)
- Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics (Q6196628) (← links)
- Approximation bounds for convolutional neural networks in operator learning (Q6403941) (← links)
- Approximation of smooth functionals using deep ReLU networks (Q6488836) (← links)
- Learning spiking neuronal networks with artificial neural networks: neural oscillations (Q6494226) (← links)
- Fourier neural operator based fluid-structure interaction for predicting the vesicle dynamics (Q6554917) (← 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)
- Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation (Q6572185) (← links)
- Koopman neural operator as a mesh-free solver of non-linear partial differential equations (Q6572200) (← links)
- Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← 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)
- Moduli of smoothness, \(K\)-functionals and Jackson-type inequalities associated with Kernel function approximation in learning theory (Q6587592) (← links)
- Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning (Q6598418) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- A discretization-invariant extension and analysis of some deep operator networks (Q6633297) (← links)
- Ensemble of physics-informed neural networks for solving plane elasticity problems with examples (Q6639905) (← links)
- Learning the Hodgkin-Huxley model with operator learning techniques (Q6641924) (← links)
- Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning (Q6652579) (← links)
- An enhanced V-cycle MgNet model for operator learning in numerical partial differential equations (Q6662449) (← links)
- Transformers as neural operators for solutions of differential equations with finite regularity (Q6669055) (← links)
