Pages that link to "Item:Q2670380"
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The following pages link to A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380):
Displaying 44 items.
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
- Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
- A heteroencoder architecture for prediction of failure locations in porous metals using variational inference (Q2160437) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems (Q2678512) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
- A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
- MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
- Deep learning phase‐field model for brittle fractures (Q6071412) (← links)
- Stochastic dynamic analysis of composite plates in thermal environments using nonlinear autoregressive model with exogenous input in polynomial chaos expansion surrogate (Q6084436) (← links)
- 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)
- A modified combined active-set Newton method for solving phase-field fracture into the monolithic limit (Q6171222) (← links)
- Variationally mimetic operator networks (Q6185143) (← links)
- Spectral operator learning for parametric PDEs without data reliance (Q6194143) (← links)
- En-DeepONet: an enrichment approach for enhancing the expressivity of neural operators with applications to seismology (Q6194144) (← links)
- 3D elastic wave propagation with a factorized Fourier neural operator (F-FNO) (Q6194185) (← links)
- A super-real-time three-dimension computing method of digital twins in space nuclear power (Q6194214) (← links)
- Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
- A physics-informed variational DeepONet for predicting the crack path in brittle materials (Q6375324) (← links)
- Peridynamic neural operators: a data-driven nonlocal constitutive model for complex material responses (Q6497150) (← links)
- RiemannONets: interpretable neural operators for Riemann problems (Q6550161) (← links)
- SPI-MIONet for surrogate modeling in phase-field hydraulic fracturing (Q6557819) (← links)
- An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385) (← links)
- Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
- A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
- Phase-field modeling of fracture with physics-informed deep learning (Q6588261) (← links)
- Transfer learning for accelerating phase-field modeling of ferroelectric domain formation in large-scale 3D systems (Q6588332) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Neural operator induced Gaussian process framework for probabilistic solution of parametric partial differential equations (Q6609778) (← links)
- Physics-informed discretization-independent deep compositional operator network (Q6609787) (← links)
- A new method to compute the blood flow equations using the physics-informed neural operator (Q6639295) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← 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)
- Separable physics-informed DeepONet: breaking the curse of dimensionality in physics-informed machine learning (Q6669073) (← links)
