Pages that link to "Item:Q2138799"
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The following pages link to A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799):
Displaying 18 items.
- Neuroscience inspired neural operator for partial differential equations (Q6614975) (← links)
- Enhancing training of physics-informed neural networks using domain decomposition-based preconditioning strategies (Q6623675) (← 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)
- A new method to compute the blood flow equations using the physics-informed neural operator (Q6639295) (← links)
- Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
- Parameter identification by deep learning of a material model for granular media (Q6643277) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Neural dynamical operator: continuous spatial-temporal model with gradient-based and derivative-free optimization methods (Q6648386) (← links)
- Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators (Q6663284) (← links)
- Kolmogorov-Arnold-informed neural network: a physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov-Arnold networks (Q6669014) (← links)
- Physics-informed geometry-aware neural operator (Q6669047) (← links)
- Transformers as neural operators for solutions of differential equations with finite regularity (Q6669055) (← links)
- Operator learning with Gaussian processes (Q6669069) (← 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)
