Digital twins that learn and correct themselves

DOI10.1002/nme.6535OpenAlexW3083377421MaRDI QIDQ6090723

Elias Cueto, I. Alfaro, Alberto Badías, Francisco Chinesta, Beatriz Moya

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.6535

zbMATH Keywords

computer visionaugmented realityscientific machine learningdigital twins

Mathematics Subject Classification ID

Numerical methods for partial differential equations, boundary value problems (65Nxx) Inference from stochastic processes (62Mxx) Optimization problems in solid mechanics (74Pxx)

Related Items (4)

A digital twin framework for civil engineering structures ⋮ A real-time variational data assimilation method with data-driven model enrichment for time-dependent problems ⋮ A thermodynamics-informed active learning approach to perception and reasoning about fluids ⋮ Physics informed and data-based augmented learning in structural health diagnosis

Cites Work

• Proper generalized decompositions. An introduction to computer implementation with Matlab
• Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models
• PGD-based \textit{computational vademecum} for efficient design, optimization and control
• Parametric solutions involving geometry: a step towards efficient shape optimization
• Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
• A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition
• Dynamic data-driven reduced-order models
• Computational reduction for parametrized PDEs: strategies and applications
• Reduced order modeling for physically-based augmented reality
• Model order reduction for real-time data assimilation through extended Kalman filters
• The proper generalized decomposition for advanced numerical simulations. A primer
• Dynamic data driven simulations in stochastic environments
• Towards hierarchical design optimization for simultaneous composite material characterization and adjustment of the corresponding physical experiments
• Fundamentals of reduced basis method for problems governed by parametrized PDEs and applications
• Transport, Collective Motion, and Brownian Motion

This page was built for publication: Digital twins that learn and correct themselves