Pages that link to "Item:Q2687573"
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The following pages link to On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573):
Displaying 8 items.
- 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)
- On the geometry transferability of the hybrid iterative numerical solver for differential equations (Q6164275) (← links)
- Learning stiff chemical kinetics using extended deep neural operators (Q6185234) (← links)
- A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
- A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
- Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
- Separable physics-informed DeepONet: breaking the curse of dimensionality in physics-informed machine learning (Q6669073) (← links)
