What question did this study set out to answer?

To explore a manifold learning approach to enhance model order reduction in quasi-static solid mechanics problems.

December 9, 2025Computational MechanicsOpen Access

A manifold learning approach to nonlinear model order reduction of quasi-static problems in solid mechanics: proof of concept and parameter study

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Authors

LSLisa ScheunemannRWTH Aachen University EFErik FaustRWTH Aachen University

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Implication

This work demonstrates the potential of manifold learning to improve model order reduction in quasi-static problems, indicating better error performance than traditional methods.

Key Points

To explore a manifold learning approach to enhance model order reduction in quasi-static solid mechanics problems.
Utilized graph-based manifold learning for nonlinear projection-based model order reduction.
Computed approximations of local tangents necessary for a Galerkin scheme during the online phase.
Conducted a parameter study using representative volume element computations.
Manifold learning approach achieved lower error compared to proper orthogonal decomposition and local basis method.
Demonstrated effectiveness over a range of model dimensionalities.

Cite This Study

Scheunemann et al. (2025) studied this question.

synapsesocial.com/papers/69401d5b2d562116f28f8c99 https://doi.org/10.1007/s00466-025-02696-0

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Implication

This work demonstrates the potential of manifold learning to improve model order reduction in quasi-static problems, indicating better error performance than traditional methods.

A manifold learning approach to nonlinear model order reduction of quasi-static problems in solid mechanics: proof of concept and parameter study

Discussion

Member takes

Implication

Key Points

Cite This Study

A manifold learning approach to nonlinear model order reduction of quasi-static problems in solid mechanics: proof of concept and parameter study

Discussion

Member takes

Implication

Key Points

Cite This Study

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