Inverse parameter estimation using compressed sensing and POD-RBF reduced order models

DOI10.1016/j.cma.2024.116820OpenAlexW4391731295MaRDI QIDQ6125474

Preetham P. Rao

Publication date: 11 April 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2024.116820

zbMATH Keywords

compressed sensingreduced order modelsinverse estimation

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Classical thermodynamics, heat transfer (80-XX)

Cites Work

• Theory and applications of the multiquadric-biharmonic method. 20 years of discovery 1968-1988
• On choosing ``optimal shape parameters for RBF approximation
• Estimation of constant thermal conductivity by use of proper orthogonal decomposition
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
• An inverse POD-RBF network approach to parameter estimation in mechanics
• Solving inverse heat conduction problems using trained POD-RBF network inverse method
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation
• Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
• Data-Driven Science and Engineering
• Proper orthogonal decomposition and modal analysis for acceleration of transient FEM thermal analysis
• Compressed sensing

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