Physics informed and data-based augmented learning in structural health diagnosis

Author:

Di Lorenzo D.ORCID,Champaney V.,Marzin J.Y.,Farhat C.,Chinesta F.

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference31 articles.

1. Feasible probabilistic learning method for model-form uncertainty quantification in vibration analysis;Farhat;AIAA J.,2019

2. A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models;Soize;Internat. J. Numer. Methods Engrg.,2016

3. Data completion, model correction and enrichment based on sparse identification and data assimilation;Di Lorenzo;Appl. Sci.,2022

4. Virtual, digital and hybrid twins: A new paradigm in data-based engineering and engineered data;Chinesta;Arch. Comput. Methods Eng.,2019

5. Digital twins that learn and correct themselves;Moya;Internat. J. Numer. Methods Engrg.,2022

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep learning in computational mechanics: a review;Computational Mechanics;2024-01-13

2. Unsupervised Learning Techniques for Vibration-Based Structural Health Monitoring Systems Driven by Data;Handbook of Research on AI and ML for Intelligent Machines and Systems;2023-11-27

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