Pages that link to "Item:Q2138717"

The following pages link to The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations (Q2138717):

Displaying 21 items.

• Neural network closures for nonlinear model order reduction (Q1756917) (← links)
• A learning-based projection method for model order reduction of transport problems (Q2088794) (← links)
• Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference (Q2106904) (← links)
• Embedded domain reduced basis models for the shallow water hyperbolic equations with the shifted boundary method (Q2160391) (← links)
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
• Neural-network-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility (Q6054198) (← links)
• POD-based reduced order methods for optimal control problems governed by parametric partial differential equation with varying boundary control (Q6096288) (← links)
• Learning proper orthogonal decomposition of complex dynamics using heavy-ball neural ODEs (Q6101554) (← links)
• Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters (Q6101879) (← links)
• A continuous convolutional trainable filter for modelling unstructured data (Q6109268) (← links)
• Front transport reduction for complex moving fronts (Q6111405) (← links)
• Forward sensitivity analysis and mode dependent control for closure modeling of Galerkin systems (Q6135189) (← links)
• Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method (Q6158995) (← 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)
• Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds (Q6194167) (← links)
• The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations (Q6375240) (← links)
• An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems (Q6549892) (← links)
• TGPT-PINN: nonlinear model reduction with transformed GPT-PINNs (Q6595863) (← links)
• A reduced-order model for advection-dominated problems based on the Radon cumulative distribution transform (Q6667677) (← links)