Pages that link to "Item:Q2678512"
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The following pages link to Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems (Q2678512):
Displaying 18 items.
- Discovering interpretable Lagrangian of dynamical systems from data (Q6086792) (← links)
- Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
- Geometric learning for computational mechanics. III: Physics-constrained response surface of geometrically nonlinear shells (Q6096461) (← links)
- Physics informed WNO (Q6120131) (← links)
- Spectral neural operators (Q6124382) (← links)
- A nonlinear-manifold reduced-order model and operator learning for partial differential equations with sharp solution gradients (Q6185246) (← links)
- 3D elastic wave propagation with a factorized Fourier neural operator (F-FNO) (Q6194185) (← links)
- Advancing wave equation analysis in dual-continuum systems: a partial learning approach with discrete empirical interpolation and deep neural networks (Q6489267) (← links)
- Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation (Q6572185) (← links)
- Render unto numerics: orthogonal polynomial neural operator for PDEs with nonperiodic boundary conditions (Q6575342) (← links)
- MODNO: multi-operator learning with distributed neural operators (Q6609751) (← links)
- Neural operator induced Gaussian process framework for probabilistic solution of parametric partial differential equations (Q6609778) (← links)
- Neuroscience inspired neural operator for partial differential equations (Q6614975) (← links)
- Learning the Hodgkin-Huxley model with operator learning techniques (Q6641924) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Differentiability in unrolled training of neural physics simulators on transient dynamics (Q6663245) (← links)
- Operator learning with Gaussian processes (Q6669069) (← links)
- Separable physics-informed DeepONet: breaking the curse of dimensionality in physics-informed machine learning (Q6669073) (← links)
