Pages that link to "Item:Q6175122"
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The following pages link to Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference (Q6175122):
Displaying 27 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)
- Projection-based model reduction: formulations for physics-based machine learning (Q1739759) (← links)
- Error estimation of the parametric non-intrusive reduced order model using machine learning (Q1988235) (← links)
- Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
- Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories (Q2050562) (← links)
- Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations (Q2072439) (← links)
- Projection-based model reduction of dynamical systems using space-time subspace and machine learning (Q2072456) (← links)
- Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
- Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
- Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
- Data-driven operator inference for nonintrusive projection-based model reduction (Q2309194) (← links)
- Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems (Q2670214) (← links)
- Operator inference and physics-informed learning of low-dimensional models for incompressible flows (Q2672189) (← links)
- LaSDI: parametric latent space dynamics identification (Q2674132) (← links)
- Bayesian operator inference for data-driven reduced-order modeling (Q2679294) (← links)
- A direct method approach for data-driven inference of high accuracy adaptive phase-isostable reduced order models (Q2688096) (← links)
- A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
- Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations (Q5864754) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation (Q6038828) (← links)
- DRIPS: a framework for dimension reduction and interpolation in parameter space (Q6048419) (← links)
- On linear models for discrete operator inference in time dependent problems (Q6157901) (← links)
- Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation (Q6159004) (← links)
- Reduced operator inference for nonlinear partial differential equations (Q6359407) (← links)
- Gradient preserving operator inference: data-driven reduced-order models for equations with gradient structure (Q6557793) (← links)
- Operator inference driven data assimilation for high fidelity neutron transport (Q6595882) (← links)
