A pruning algorithm preserving modeling capabilities for polycrystalline data

Farooq, Harris; Ryckelynck, David; Forest, Samuel; Cailletaud, Georges; Marano, Aldo

doi:10.1007/s00466-021-02075-5

Comput Mech

2021

DOI: 10.1007/s00466-021-02075-5

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A pruning algorithm preserving modeling capabilities for polycrystalline data

Harris Farooq

David Ryckelynck

Samuel Forest

et al.

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2024

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Learning Projection-Based Reduced-Order Models

Ryckelynck,

Casenave,

Akkari

2024

SpringerBriefs in Computer Science

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In this chapter, we introduce the solution space for high-fidelity models based on partial differential equations and the finite element model. The manifold learning approach to model order reduction requires simulated data. Hence, learning projection-based reduced order models (ROM) has two steps: (i) an offline step for the computation of simulated data and for consecutive machine learning tasks, (ii) an online step where the reduced order model is used as a surrogate for the high fidelity model. The offline step generates a train set and a validation set of simulated data. The accuracy and the generalisation of the reduced order model is evaluated in the online step by using a test set of data forecast by the high-fidelity model. The test set aims also to check the computational speedups of the reduced-order model compare to the high-fidelity model.

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Learning Projection-Based Reduced-Order Models

Ryckelynck,

Casenave,

Akkari

2024

SpringerBriefs in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

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A pruning algorithm preserving modeling capabilities for polycrystalline data

Cited by 1 publication

References 50 publications

Learning Projection-Based Reduced-Order Models

Learning Projection-Based Reduced-Order Models

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