Pages that link to "Item:Q2679470"
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The following pages link to SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470):
Displaying 8 items.
- Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
- Learning stiff chemical kinetics using extended deep neural operators (Q6185234) (← links)
- Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems (Q6194148) (← links)
- SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability (Q6397563) (← links)
- RiemannONets: interpretable neural operators for Riemann problems (Q6550161) (← links)
- On the training and generalization of deep operator networks (Q6573171) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Transformers as neural operators for solutions of differential equations with finite regularity (Q6669055) (← links)
