Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping

Cites Work

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
Regularized ensemble Kalman methods for inverse problems
Optimal sensor placement using machine learning
Approach for uncertainty of turbulence modeling based on data assimilation technique
Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes
Enforcing boundary conditions on physical fields in Bayesian inversion
Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF
Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows
A physics-informed diffusion model for high-fidelity flow field reconstruction
A cache-efficient reordering method for unstructured meshes with applications to wall-resolved large-eddy simulations
Ensemble Kalman methods for inverse problems
State estimation in wall-bounded flow systems. Part 3. The ensemble Kalman filter
Turbulent Flows
Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder
A bilevel learning approach for optimal observation placement in variational data assimilation
Optimal sensor and actuator placement for feedback control of vortex shedding
Ensemble Kalman method for learning turbulence models from indirect observation data
Ensemble Gradient for Learning Turbulence Models from Indirect Observations
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Unsupervised deep learning for super-resolution reconstruction of turbulence
An Information Criterion for Choosing Observation Locations in Data Assimilation and Prediction
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion
The ensemble Kalman filter for combined state and parameter estimation
Combining direct and indirect sparse data for learning generalizable turbulence models

This page was built for publication: Optimal sensor placement for ensemble-based data assimilation using gradient-weighted class activation mapping