Pages that link to "Item:Q5364198"
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The following pages link to Data-Driven Reduced Model Construction with Time-Domain Loewner Models (Q5364198):
Displaying 29 items.
- Projection-based model reduction: formulations for physics-based machine learning (Q1739759) (← links)
- Minimal realization and approximation of commensurate linear fractional-order systems via Loewner matrix method (Q1981029) (← links)
- A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems (Q2020779) (← links)
- Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms (Q2021063) (← links)
- Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations (Q2072439) (← links)
- Intrusive and data-driven reduced order modelling of the rotating thermal shallow water equation (Q2079107) (← links)
- A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings (Q2129328) (← links)
- Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations (Q2158140) (← links)
- Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
- Data-driven constrained optimal model reduction (Q2198730) (← links)
- Identification of port-Hamiltonian systems from frequency response data (Q2203466) (← links)
- Robust nonlinear processing of active array data in inverse scattering via truncated reduced order models (Q2214554) (← links)
- Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
- Operator inference and physics-informed learning of low-dimensional models for incompressible flows (Q2672189) (← links)
- A non-intrusive method to inferring linear port-Hamiltonian realizations using time-domain data (Q2672195) (← links)
- Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference (Q2678552) (← links)
- 6 The Loewner framework for system identification and reduction (Q3384276) (← links)
- On Bilinear Time-Domain Identification and Reduction in the Loewner Framework (Q5014011) (← links)
- Data-Driven Balancing of Linear Dynamical Systems (Q5037546) (← links)
- Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation (Q5049221) (← links)
- Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference (Q5146677) (← links)
- A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods (Q6060947) (← links)
- Port-Hamiltonian Dynamic Mode Decomposition (Q6116390) (← links)
- Operator inference with roll outs for learning reduced models from scarce and low-quality data (Q6135185) (← links)
- A data-driven Krylov model order reduction for large-scale dynamical systems (Q6159286) (← links)
- Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems (Q6495874) (← links)
- On the sample complexity of stabilizing linear dynamical systems from data (Q6566152) (← links)
- Data-driven model reduction by two-sided moment matching (Q6574441) (← links)
- AAA rational approximation for time domain model order reduction (Q6595456) (← links)
