Pages that link to "Item:Q5058646"
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The following pages link to A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646):
Displaying 26 items.
- Data driven approximation of parametrized PDEs by reduced basis and neural networks (Q782002) (← links)
- A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
- Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
- Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
- Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
- Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
- A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
- A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← links)
- Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335) (← links)
- Leveraging reduced-order models for state estimation using deep learning (Q5113091) (← links)
- The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
- Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations (Q5864754) (← links)
- A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
- Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)
- Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
- Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions (Q6097611) (← links)
- Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method (Q6158995) (← links)
- Approximation bounds for convolutional neural networks in operator learning (Q6403941) (← links)
- Meta-auto-decoder: a meta-learning-based reduced order model for solving parametric partial differential equations (Q6575297) (← links)
- Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Q6581233) (← links)
- TGPT-PINN: nonlinear model reduction with transformed GPT-PINNs (Q6595863) (← links)
- On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields (Q6624464) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
- Parametric model reduction with convolutional neural networks (Q6648521) (← links)
- Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning (Q6652579) (← links)
- Application of deep learning reduced-order modeling for single-phase flow in faulted porous media (Q6662482) (← links)
