Pages that link to "Item:Q5048574"
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The following pages link to MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574):
Displaying 24 items.
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness (Q6084435) (← links)
- Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads (Q6096499) (← links)
- Reliable extrapolation of deep neural operators informed by physics or sparse observations (Q6097626) (← links)
- Transferable neural networks for partial differential equations (Q6123346) (← links)
- Variationally mimetic operator networks (Q6185143) (← links)
- A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients (Q6185246) (← links)
- SPI-MIONet for surrogate modeling in phase-field hydraulic fracturing (Q6557819) (← links)
- D2NO: efficient handling of heterogeneous input function spaces with distributed deep neural operators (Q6566058) (← links)
- On the training and generalization of deep operator networks (Q6573171) (← links)
- Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)
- A failure-informed multi-stage training algorithm for three-component nonlinear Schrödinger equation (Q6585366) (← links)
- Solving parametric elliptic interface problems via interfaced operator network (Q6589882) (← links)
- Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
- Neural and spectral operator surrogates: unified construction and expression rate bounds (Q6601288) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- A transfer learning physics-informed deep learning framework for modeling multiple solute dynamics in unsaturated soils (Q6609790) (← links)
- Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← 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)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators (Q6663284) (← links)
- Separable physics-informed DeepONet: breaking the curse of dimensionality in physics-informed machine learning (Q6669073) (← links)
- Conformalized-DeepONet: a distribution-free framework for uncertainty quantification in deep operator networks (Q6669489) (← links)
