Pages that link to "Item:Q2149522"
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The following pages link to Improved architectures and training algorithms for deep operator networks (Q2149522):
Displaying 15 items.
- SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
- MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads (Q6096499) (← links)
- A multifidelity deep operator network approach to closure for multiscale systems (Q6116145) (← links)
- NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
- En-DeepONet: an enrichment approach for enhancing the expressivity of neural operators with applications to seismology (Q6194144) (← links)
- Kernel methods are competitive for operator learning (Q6202132) (← links)
- Improved architectures and training algorithms for deep operator networks (Q6379358) (← links)
- On the locality of local neural operator in learning fluid dynamics (Q6557796) (← links)
- On the training and generalization of deep operator networks (Q6573171) (← links)
- Physics-informed discretization-independent deep compositional operator network (Q6609787) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
- Physics-informed geometry-aware neural operator (Q6669047) (← links)
