Pages that link to "Item:Q1987897"
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The following pages link to Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations (Q1987897):
Displaying 16 items.
- A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
- Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification (Q1787655) (← links)
- Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism (Q2123923) (← links)
- A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467) (← links)
- Time-series machine-learning error models for approximate solutions to parameterized dynamical systems (Q2184303) (← links)
- Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
- Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
- Model reduction for the material point method via an implicit neural representation of the deformation map (Q2687512) (← links)
- (Q5043153) (← links)
- A framework for machine learning of model error in dynamical systems (Q6076655) (← links)
- Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling (Q6097872) (← links)
- Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations (Q6101904) (← links)
- Deep learning-based schemes for singularly perturbed convection-diffusion problems (Q6127045) (← links)
- Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
- From Halley to secant: redefining root finding with memory-based methods including convergence and stability (Q6559945) (← links)
- Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Q6581233) (← links)
