Pages that link to "Item:Q5019423"
From MaRDI portal
The following pages link to Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (Q5019423):
Displaying 37 items.
- Sparse Principal Component Analysis via Variable Projection (Q150980) (← links)
- An extended DEIM algorithm for subset selection and class identification (Q2051261) (← links)
- Feasibility of DEIM for retrieving the initial field via dimensionality reduction (Q2120024) (← links)
- Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
- Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems: a new approach using secants (Q2163754) (← links)
- Reduced order modeling of time-dependent incompressible Navier-Stokes equation with variable density based on a local radial basis functions-finite difference (LRBF-FD) technique and the POD/DEIM method (Q2180431) (← links)
- Adaptive sparse interpolation for accelerating nonlinear stochastic reduced-order modeling with time-dependent bases (Q2683419) (← links)
- Retrieval of initial condition for Burgers' equation using reduced-order EnKF via POD-based sparse observations (Q2691379) (← links)
- Optimized Sampling for Multiscale Dynamics (Q4627447) (← links)
- Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation (Q4957372) (← links)
- Data-driven prediction of multistable systems from sparse measurements (Q5000863) (← links)
- Sensor Placement Sensitivity and Robust Reconstruction of Wave Dynamics from Multiple Sensors (Q5056838) (← links)
- Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
- Efficient Sensor Placement for Signal Reconstruction Based on Recursive Methods (Q5103464) (← links)
- Leveraging reduced-order models for state estimation using deep learning (Q5113091) (← links)
- An Information Criterion for Choosing Observation Locations in Data Assimilation and Prediction (Q5149776) (← links)
- Shallow neural networks for fluid flow reconstruction with limited sensors (Q5160984) (← links)
- Randomized Dynamic Mode Decomposition (Q5207530) (← links)
- Reinforcement-learning-based control of convectively unstable flows (Q5870478) (← links)
- An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement (Q5886849) (← links)
- Interpolatory input and output projections for flow control (Q6077957) (← links)
- Bayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary framework (Q6092206) (← links)
- Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification (Q6095126) (← links)
- A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (Q6109162) (← links)
- Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data (Q6113944) (← links)
- Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations (Q6117701) (← links)
- Inverse parameter estimation using compressed sensing and POD-RBF reduced order models (Q6125474) (← links)
- Forward sensitivity analysis and mode dependent control for closure modeling of Galerkin systems (Q6135189) (← links)
- WSN optimization for sampling-based signal estimation using semi-binarized variational autoencoder (Q6154778) (← links)
- Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations (Q6196594) (← links)
- Launching Drifter Observations in the Presence of Uncertainty (Q6444907) (← links)
- Efficient grid deformation using deterministic sampling-based data reduction (Q6553393) (← links)
- Sparse sensor selection for distributed systems: an \(l_1\)-relaxation approach (Q6566755) (← links)
- A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation (Q6584213) (← links)
- SeAr PC: sensitivity enhanced arbitrary polynomial chaos (Q6609782) (← links)
- Physics-informed machine learning for the inverse design of wave scattering clusters (Q6632907) (← links)
- Sparse discrete empirical interpolation method: state estimation from few sensors (Q6649887) (← links)
