Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models
From MaRDI portal
Publication:6090720
DOI10.1002/nme.6423OpenAlexW3026573367MaRDI QIDQ6090720
No author found.
Publication date: 17 November 2023
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/nme.6423
Parametric inference (62Fxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Controllability, observability, and system structure (93Bxx)
Related Items
A new method to interpolate POD reduced bases–Application to the parametric model order reduction of a gas bearings supported rotor ⋮ Predictive reduced order modeling of chaotic multi-scale problems using adaptively sampled projections ⋮ Localized Model Reduction for Nonlinear Elliptic Partial Differential Equations: Localized Training, Partition of Unity, and Adaptive Enrichment ⋮ A digital twin framework for civil engineering structures ⋮ Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems ⋮ Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure ⋮ Optimal Local Approximation Spaces for Parabolic Problems
Cites Work
- Unnamed Item
- Reduced order methods for modeling and computational reduction
- Bayesian inference with optimal maps
- A new certification framework for the port reduced static condensation reduced basis element method
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- A component-based hybrid reduced basis/finite element method for solid mechanics with local nonlinearities
- Bayesian Calibration of Computer Models
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Optimal Local Approximation Spaces for Component-Based Static Condensation Procedures
- A Static condensation Reduced Basis Element method : approximation anda posteriorierror estimation
- Port reduction in parametrized component static condensation: approximation and a posteriori error estimation
- A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection
- Convergence Rates for Greedy Algorithms in Reduced Basis Methods
- Approximation of Large-Scale Dynamical Systems
- Estimation of the Error in the Reduced Basis Method Solution of Nonlinear Equations
- Secure State Estimation Against Sensor Attacks in the Presence of Noise
- Attack-Resilient State Estimation for Noisy Dynamical Systems
- Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models
This page was built for publication: Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models
